Calculating pace and energy expenditure from athletic movement attributes

ABSTRACT

Systems and methods configured to process motion data associated with a user. The systems and methods are configured to receive motion data from a sensor, calculate motion attributes from the data, and classify the motion data using one or more mathematical models. Attributes may be calculated without classifying the motion data into an activity. Attributes may be compared to mathematical models. Motion data within the models and attributes of the user may be independent of any activity type. Attributes may be compared to select an energy expenditure model from one or more energy expenditure models, or an activity classification model, from the one or more activity classification models. An energy expenditure, or a classification of received data as a linear travel motion, may then be calculated.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Patent Application No.61/890,748, entitled “CALCULATING ENERGY EXPENDITURE FROM ATHLETICMOVEMENTS,” filed Oct. 14, 2013, and U.S. Provisional Patent ApplicationNo. 61/900,203, entitled “CALCULATING PACE AND ENERGY EXPENDITURE FROMATHLETIC MOVEMENT ATTRIBUTES,” filed on Nov. 5, 2013, which areexpressly incorporated herein by reference in their entireties for anyand all non-limiting purposes.

BACKGROUND

While most people appreciate the importance of physical fitness, manyhave difficulty finding the motivation required to maintain a regularexercise program. Some people find it particularly difficult to maintainan exercise regimen that involves continuously repetitive motions, suchas running, walking and bicycling.

Additionally, individuals may view exercise as work or a chore and thus,separate it from enjoyable aspects of their daily lives. Often, thisclear separation between athletic activity and other activities reducesthe amount of motivation that an individual might have towardexercising. Further, athletic activity services and systems directedtoward encouraging individuals to engage in athletic activities mightalso be too focused on one or more particular activities while anindividual's interest are ignored. This may further decrease a user'sinterest in participating in athletic activities or using athleticactivity services and systems.

Many existing services and devices fail to provide accurate assessmentof the user's energy expenditure, such as caloric burn, during physicalactivity. Therefore, users are unaware of the benefits that certainactivities, which may include daily routines that are often not thoughtof as being a “workout”, are to their health. Existing devices forallowing users to monitor their energy expenditure often suffer from oneor more deficiencies, including: cumbersome collection systems,inaccurate measurements that are beyond an acceptable threshold,unacceptable latency in reporting the values, erroneous classificationof activities based upon detected motions of the user, failure toaccount for deviations between different users, improperly includingrepetitive behavior as being classified as a specific activity, such asfor example, running and/or walking, relatively high power consumption,and/or a combination of these or other deficiencies.

Therefore, improved systems and methods to address at least one or moreof these shortcomings in the art are desired.

BRIEF SUMMARY

The following presents a simplified summary of the present disclosure inorder to provide a basic understanding of some aspects of the invention.This summary is not an extensive overview of the invention. It is notintended to identify key or critical elements of the invention or todelineate the scope of the invention. The following summary merelypresents some concepts of the invention in a simplified form as aprelude to the more detailed description provided below.

Aspects relate to systems and methods configured to process motion dataassociated with a user. Motion data may be utilized, such as for exampletransformed, to calculate motion attributes from the data. The sensormay be worn by the user in certain embodiments. In certainimplementations, it may be worn on an appendage of the user. In furtherembodiments, at least one sensor is not in physical contact with theuser.

One or more motion attributes from a dataset may be calculated withoutclassifying the motion data into an activity type (such as walking,running, swimming, or any specific or general activity). One or moremotion attributes may be compared to one or more models comprisingmotion data from a plurality of individuals. The plurality ofindividuals may not include the user. In certain embodiments, both themotion data within the models and the motion attributes of the user areindependent of any activity type. Various models may be used tocalculate different values. For example, in certain embodiments, theattributes may be compared to select an energy expenditure model fromthe one or more energy expenditure models, or to select an activityclassification model, from one or more activity classification models,which may be selected as a best-match to the one or more motionattributes. The selected model may then be used to calculate an energyexpenditure or to classify a linear travel motion associated with themotion of the user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system that may be configured to providepersonal training and/or obtain data from the physical movements of auser in accordance with example embodiments;

FIG. 2 illustrates an example computer device that may be part of or incommunication with the system of FIG. 1.

FIG. 3 shows an illustrative sensor assembly that may be worn by a userin accordance with example embodiments;

FIG. 4 shows another example sensor assembly that may be worn by a userin accordance with example embodiments;

FIG. 5 shows illustrative locations for sensory input which may includephysical sensors located on/in a user's clothing and/or be based uponidentification of relationships between two moving body parts of theuser;

FIG. 6 shows an example flowchart that may be implemented to metrics,including energy expenditure and speed.

FIG. 7 shows a flowchart that may be implemented to validate receivedsensor data in accordance with one embodiment;

FIGS. 8A-8B show flowchart diagrams that may be implemented to transformreceived sensor data in accordance with one embodiment;

FIGS. 9A-9E show flowchart diagrams that may be implemented to calculateattributes from transformed sensor data in accordance with oneembodiment;

FIG. 10 shows an example flowchart that may estimate frequency and setup a frequency search range in accordance with one embodiment;

FIG. 11 shows a graph illustrating an example search range of motiondata in accordance with certain embodiments;

FIGS. 12A and 12B show a graph illustrating a sample FFT output.Specifically, FIG. 12A shows a graph plotting FFT power againstfrequency data that includes data within an arm swing range and datawithin the bounce range; and FIG. 12B shows the same graph with athreshold utilized to determine if peaks within the bounce range meet acriterion in accordance with one implementation;

FIGS. 13A and 13B show example flowcharts that may be implemented indeterminations of whether to utilize arm swing frequency, bouncefrequency and/or other frequencies in accordance with one embodiment;

FIG. 14 shows a flowchart diagram that may be implemented to comparecalculated attributes to expert models used to predict one or moreoutput values associated with activities performed by a user;

FIG. 15A-15C depict flowchart diagrams that may be implemented tocategorize an athlete's activity in accordance with one embodiment;

FIG. 16 is a flowchart that may be implemented to categorize an certainactivity as either walking or running in accordance with one embodiment;and

FIGS. 17A-17E show flowchart diagrams that may be implemented tocalculate attributes from transformed sensor data in accordance with oneembodiment.

DETAILED DESCRIPTION

Aspects of this disclosure involve obtaining, storing, and/or processingathletic data relating to the physical movements of an athlete. Theathletic data may be actively or passively sensed and/or stored in oneor more non-transitory storage mediums. Still further aspects relate tousing athletic data to generate an output, such as for example,calculated athletic attributes, feedback signals to provide guidance,and/or other information. These and other aspects will be discussed inthe context of the following illustrative examples of a personaltraining system.

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration various embodiments in whichaspects of the disclosure may be practiced. It is to be understood thatother embodiments may be utilized and structural and functionalmodifications may be made without departing from the scope and spirit ofthe present disclosure. Further, headings within this disclosure shouldnot be considered as limiting aspects of the disclosure and the exampleembodiments are not limited to the example headings.

I. Example Personal Training System

A. Illustrative Networks

Aspects of this disclosure relate to systems and methods that may beutilized across a plurality of networks. In this regard, certainembodiments may be configured to adapt to dynamic network environments.Further embodiments may be operable in differing discrete networkenvironments. FIG. 1 illustrates an example of a personal trainingsystem 100 in accordance with example embodiments. Example system 100may include one or more interconnected networks, such as theillustrative body area network (BAN) 102, local area network (LAN) 104,and wide area network (WAN) 106. As shown in FIG. 1 (and describedthroughout this disclosure), one or more networks (e.g., BAN 102, LAN104, and/or WAN 106), may overlap or otherwise be inclusive of eachother. Those skilled in the art will appreciate that the illustrativenetworks 102-106 are logical networks that may each comprise one or moredifferent communication protocols and/or network architectures and yetmay be configured to have gateways to each other or other networks. Forexample, each of BAN 102, LAN 104 and/or WAN 106 may be operativelyconnected to the same physical network architecture, such as cellularnetwork architecture 108 and/or WAN architecture 110. For example,portable electronic device 112, which may be considered a component ofboth BAN 102 and LAN 104, may comprise a network adapter or networkinterface card (NIC) configured to translate data and control signalsinto and from network messages according to one or more communicationprotocols, such as the Transmission Control Protocol (TCP), the InternetProtocol (IP), and the User Datagram Protocol (UDP) through one or moreof architectures 108 and/or 110. These protocols are well known in theart, and thus will not be discussed here in more detail.

Network architectures 108 and 110 may include one or more informationdistribution network(s), of any type(s) or topology(s), alone or incombination(s), such as for example, cable, fiber, satellite, telephone,cellular, wireless, etc. and as such, may be variously configured suchas having one or more wired or wireless communication channels(including but not limited to: WiFi®, Bluetooth®, Near-FieldCommunication (NFC) and/or ANT technologies). Thus, any device within anetwork of FIG. 1, (such as portable electronic device 112 or any otherdevice described herein) may be considered inclusive to one or more ofthe different logical networks 102-106. With the foregoing in mind,example components of an illustrative BAN and LAN (which may be coupledto WAN 106) will be described.

1. Example Local Area Network

LAN 104 may include one or more electronic devices, such as for example,computer device 114. Computer device 114, or any other component ofsystem 100, may comprise a mobile terminal, such as a telephone, musicplayer, tablet, netbook or any portable device. In other embodiments,computer device 114 may comprise a media player or recorder, desktopcomputer, server(s), a gaming console, such as for example, a Microsoft®XBOX, Sony® Playstation, and/or a Nintendo® Wii gaming consoles. Thoseskilled in the art will appreciate that these are merely example devicesfor descriptive purposes and this disclosure is not limited to anyconsole or computing device.

Those skilled in the art will appreciate that the design and structureof computer device 114 may vary depending on several factors, such asits intended purpose. One example implementation of computer device 114is provided in FIG. 2, which illustrates a block diagram of computingdevice 200. Those skilled in the art will appreciate that the disclosureof FIG. 2 may be applicable to any device disclosed herein. Device 200may include one or more processors, such as processor 202-1 and 202-2(generally referred to herein as “processors 202” or “processor 202”).Processors 202 may communicate with each other or other components viaan interconnection network or bus 204. Processor 202 may include one ormore processing cores, such as cores 206-1 and 206-2 (referred to hereinas “cores 206” or more generally as “core 206”), which may beimplemented on a single integrated circuit (IC) chip.

Cores 206 may comprise a shared cache 208 and/or a private cache (e.g.,caches 210-1 and 210-2, respectively). One or more caches 208/210 maylocally cache data stored in a system memory, such as memory 212, forfaster access by components of the processor 202. Memory 212 may be incommunication with the processors 202 via a chipset 216. Cache 208 maybe part of system memory 212 in certain embodiments. Memory 212 mayinclude, but is not limited to, random access memory (RAM), read onlymemory (ROM), and include one or more of solid-state memory, optical ormagnetic storage, and/or any other medium that can be used to storeelectronic information. Yet other embodiments may omit system memory212.

System 200 may include one or more I/O devices (e.g., I/O devices 214-1through 214-3, each generally referred to as I/O device 214). I/O datafrom one or more I/O devices 214 may be stored at one or more caches208, 210 and/or system memory 212. Each of I/O devices 214 may bepermanently or temporarily configured to be in operative communicationwith a component of system 100 using any physical or wirelesscommunication protocol.

Returning to FIG. 1, four example I/O devices (shown as elements116-122) are shown as being in communication with computer device 114.Those skilled in the art will appreciate that one or more of devices116-122 may be stand-alone devices or may be associated with anotherdevice besides computer device 114. For example, one or more I/O devicesmay be associated with or interact with a component of BAN 102 and/orWAN 106. I/O devices 116-122 may include, but are not limited toathletic data acquisition units, such as for example, sensors. One ormore I/O devices may be configured to sense, detect, and/or measure anathletic parameter from a user, such as user 124. Examples include, butare not limited to: an accelerometer, a gyroscope, alocation-determining device (e.g., GPS), light (including non-visiblelight) sensor, temperature sensor (including ambient temperature and/orbody temperature), sleep pattern sensors, heart rate monitor,image-capturing sensor, moisture sensor, force sensor, compass, angularrate sensor, and/or combinations thereof among others.

In further embodiments, I/O devices 116-122 may be used to provide anoutput (e.g., audible, visual, or tactile cue) and/or receive an input,such as a user input from athlete 124. Example uses for theseillustrative I/O devices are provided below, however, those skilled inthe art will appreciate that such discussions are merely descriptive ofsome of the many options within the scope of this disclosure. Further,reference to any data acquisition unit, I/O device, or sensor is to beinterpreted disclosing an embodiment that may have one or more I/Odevice, data acquisition unit, and/or sensor disclosed herein or knownin the art (either individually or in combination).

Information from one or more devices (across one or more networks) maybe used to provide (or be utilized in the formation of) a variety ofdifferent parameters, metrics or physiological characteristics includingbut not limited to: motion parameters, such as speed, acceleration,distance, steps taken, direction, relative movement of certain bodyportions or objects to others, or other motion parameters which may beexpressed as angular rates, rectilinear rates or combinations thereof,physiological parameters, such as calories, heart rate, sweat detection,effort, oxygen consumed, oxygen kinetics, and other metrics which mayfall within one or more categories, such as: pressure, impact forces,information regarding the athlete, such as height, weight, age,demographic information and combinations thereof.

System 100 may be configured to transmit and/or receive athletic data,including the parameters, metrics, or physiological characteristicscollected within system 100 or otherwise provided to system 100. As oneexample, WAN 106 may comprise server 111. Server 111 may have one ormore components of system 200 of FIG. 2. In one embodiment, server 111comprises at least a processor and a memory, such as processor 206 andmemory 212. Server 111 may be configured to store computer-executableinstructions on a non-transitory computer-readable medium. Theinstructions may comprise athletic data, such as raw or processed datacollected within system 100. System 100 may be configured to transmitdata, such as energy expenditure points, to a social networking websiteor host such a site. Server 111 may be utilized to permit one or moreusers to access and/or compare athletic data. As such, server 111 may beconfigured to transmit and/or receive notifications based upon athleticdata or other information.

Returning to LAN 104, computer device 114 is shown in operativecommunication with a display device 116, an image-capturing device 118,sensor 120 and exercise device 122, which are discussed in turn belowwith reference to example embodiments. In one embodiment, display device116 may provide audio-visual cues to athlete 124 to perform a specificathletic movement. The audio-visual cues may be provided in response tocomputer-executable instruction executed on computer device 114 or anyother device, including a device of BAN 102 and/or WAN. Display device116 may be a touchscreen device or otherwise configured to receive auser-input.

In one embodiment, data may be obtained from image-capturing device 118and/or other sensors, such as sensor 120, which may be used to detect(and/or measure) athletic parameters, either alone or in combinationwith other devices, or stored information. Image-capturing device 118and/or sensor 120 may comprise a transceiver device. In one embodimentsensor 128 may comprise an infrared (IR), electromagnetic (EM) oracoustic transceiver. For example, image-capturing device 118, and/orsensor 120 may transmit waveforms into the environment, includingtowards the direction of athlete 124 and receive a “reflection” orotherwise detect alterations of those released waveforms. Those skilledin the art will readily appreciate that signals corresponding to amultitude of different data spectrums may be utilized in accordance withvarious embodiments. In this regard, devices 118 and/or 120 may detectwaveforms emitted from external sources (e.g., not system 100). Forexample, devices 118 and/or 120 may detect heat being emitted from user124 and/or the surrounding environment. Thus, image-capturing device 126and/or sensor 128 may comprise one or more thermal imaging devices. Inone embodiment, image-capturing device 126 and/or sensor 128 maycomprise an IR device configured to perform range phenomenology.

In one embodiment, exercise device 122 may be any device configurable topermit or facilitate the athlete 124 performing a physical movement,such as for example a treadmill, step machine, etc. There is norequirement that the device be stationary. In this regard, wirelesstechnologies permit portable devices to be utilized, thus a bicycle orother mobile exercising device may be utilized in accordance withcertain embodiments. Those skilled in the art will appreciate thatequipment 122 may be or comprise an interface for receiving anelectronic device containing athletic data performed remotely fromcomputer device 114. For example, a user may use a sporting device(described below in relation to BAN 102) and upon returning home or thelocation of equipment 122, download athletic data into element 122 orany other device of system 100. Any I/O device disclosed herein may beconfigured to receive activity data.

2. Body Area Network

BAN 102 may include two or more devices configured to receive, transmit,or otherwise facilitate the collection of athletic data (includingpassive devices). Exemplary devices may include one or more dataacquisition units, sensors, or devices known in the art or disclosedherein, including but not limited to I/O devices 116-122. Two or morecomponents of BAN 102 may communicate directly, yet in otherembodiments, communication may be conducted via a third device, whichmay be part of BAN 102, LAN 104, and/or WAN 106. One or more componentsof LAN 104 or WAN 106 may form part of BAN 102. In certainimplementations, whether a device, such as portable device 112, is partof BAN 102, LAN 104, and/or WAN 106, may depend on the athlete'sproximity to an access point to permit communication with mobilecellular network architecture 108 and/or WAN architecture 110. Useractivity and/or preference may also influence whether one or morecomponents are utilized as part of BAN 102. Example embodiments areprovided below.

User 124 may be associated with (e.g., possess, carry, wear, and/orinteract with) any number of devices, such as portable device 112,shoe-mounted device 126, wrist-worn device 128 and/or a sensinglocation, such as sensing location 130, which may comprise a physicaldevice or a location that is used to collect information. One or moredevices 112, 126, 128, and/or 130 may not be specially designed forfitness or athletic purposes. Indeed, aspects of this disclosure relateto utilizing data from a plurality of devices, some of which are notfitness devices, to collect, detect, and/or measure athletic data. Incertain embodiments, one or more devices of BAN 102 (or any othernetwork) may comprise a fitness or sporting device that is specificallydesigned for a particular sporting use. As used herein, the term“sporting device” includes any physical object that may be used orimplicated during a specific sport or fitness activity. Exemplarysporting devices may include, but are not limited to: golf balls,basketballs, baseballs, soccer balls, footballs, powerballs, hockeypucks, weights, bats, clubs, sticks, paddles, mats, and combinationsthereof. In further embodiments, exemplary fitness devices may includeobjects within a sporting environment where a specific sport occurs,including the environment itself, such as a goal net, hoop, backboard,portions of a field, such as a midline, outer boundary marker, base, andcombinations thereof.

In this regard, those skilled in the art will appreciate that one ormore sporting devices may also be part of (or form) a structure andvice-versa, a structure may comprise one or more sporting devices or beconfigured to interact with a sporting device. For example, a firststructure may comprise a basketball hoop and a backboard, which may beremovable and replaced with a goal post. In this regard, one or moresporting devices may comprise one or more sensors, such as one or moreof the sensors discussed above in relation to FIGS. 1-3, that mayprovide information utilized, either independently or in conjunctionwith other sensors, such as one or more sensors associated with one ormore structures. For example, a backboard may comprise a first sensorconfigured to measure a force and a direction of the force by abasketball upon the backboard and the hoop may comprise a second sensorto detect a force. Similarly, a golf club may comprise a first sensorconfigured to detect grip attributes on the shaft and a second sensorconfigured to measure impact with a golf ball.

Looking to the illustrative portable device 112, it may be amulti-purpose electronic device, that for example, includes a telephoneor digital music player, including an IPOD®, IPAD®, or iPhone®, branddevices available from Apple, Inc. of Cupertino, Calif. or Zune® orMicrosoft® Windows devices available from Microsoft of Redmond, Wash. Asknown in the art, digital media players can serve as an output device,input device, and/or storage device for a computer. Device 112 may beconfigured as an input device for receiving raw or processed datacollected from one or more devices in BAN 102, LAN 104, or WAN 106. Inone or more embodiments, portable device 112 may comprise one or morecomponents of computer device 114. For example, portable device 112 maybe include a display 116, image-capturing device 118, and/or one or moredata acquisition devices, such as any of the I/O devices 116-122discussed above, with or without additional components, so as tocomprise a mobile terminal.

a. Illustrative Apparel/Accessory Sensors

In certain embodiments, I/O devices may be formed within or otherwiseassociated with user's 124 clothing or accessories, including a watch,armband, wristband, necklace, shirt, shoe, or the like. These devicesmay be configured to monitor athletic movements of a user. It is to beunderstood that they may detect athletic movement during user's 124interactions with computer device 114 and/or operate independently ofcomputer device 114 (or any other device disclosed herein). For example,one or more devices in BAN 102 may be configured to function as anall-day activity monitor that measures activity regardless of the user'sproximity or interactions with computer device 114. It is to be furtherunderstood that the sensory system 302 shown in FIG. 3 and the deviceassembly 400 shown in FIG. 4, each of which are described in thefollowing paragraphs, are merely illustrative examples.

i. Shoe-Mounted Device

In certain embodiments, device 126 shown in FIG. 1, may comprisefootwear which may include one or more sensors, including but notlimited to those disclosed herein and/or known in the art. FIG. 3illustrates one example embodiment of a sensor system 302 providing oneor more sensor assemblies 304. Assembly 304 may comprise one or moresensors, such as for example, an accelerometer, gyroscope,location-determining components, force sensors and/or or any othersensor disclosed herein or known in the art. In the illustratedembodiment, assembly 304 incorporates a plurality of sensors, which mayinclude force-sensitive resistor (FSR) sensors 306; however, othersensor(s) may be utilized. Port 308 may be positioned within a solestructure 309 of a shoe, and is generally configured for communicationwith one or more electronic devices. Port 308 may optionally be providedto be in communication with an electronic module 310, and the solestructure 309 may optionally include a housing 311 or other structure toreceive the module 310. The sensor system 302 may also include aplurality of leads 312 connecting the FSR sensors 306 to the port 308,to enable communication with the module 310 and/or another electronicdevice through the port 308. Module 310 may be contained within a wellor cavity in a sole structure of a shoe, and the housing 311 may bepositioned within the well or cavity. In one embodiment, at least onegyroscope and at least one accelerometer are provided within a singlehousing, such as module 310 and/or housing 311. In at least a furtherembodiment, one or more sensors are provided that, when operational, areconfigured to provide directional information and angular rate data. Theport 308 and the module 310 include complementary interfaces 314, 316for connection and communication.

In certain embodiments, at least one force-sensitive resistor 306 shownin FIG. 3 may contain first and second electrodes or electrical contacts318, 320 and a force-sensitive resistive material 322 disposed betweenthe electrodes 318, 320 to electrically connect the electrodes 318, 320together. When pressure is applied to the force-sensitive material 322,the resistivity and/or conductivity of the force-sensitive material 322changes, which changes the electrical potential between the electrodes318, 320. The change in resistance can be detected by the sensor system302 to detect the force applied on the sensor 316. The force-sensitiveresistive material 322 may change its resistance under pressure in avariety of ways. For example, the force-sensitive material 322 may havean internal resistance that decreases when the material is compressed.Further embodiments may utilize “volume-based resistance”, which may beimplemented through “smart materials.” As another example, the material322 may change the resistance by changing the degree ofsurface-to-surface contact, such as between two pieces of the forcesensitive material 322 or between the force sensitive material 322 andone or both electrodes 318, 320. In some circumstances, this type offorce-sensitive resistive behavior may be described as “contact-basedresistance.”

ii. Wrist-Worn Device

As shown in FIG. 4, device 400 (which may resemble or comprise sensorydevice 128 shown in FIG. 1), may be configured to be worn by user 124,such as around a wrist, arm, ankle, neck or the like. Device 400 mayinclude an input mechanism, such as a depressible input button 402configured to be used during operation of the device 400. The inputbutton 402 may be operably connected to a controller 404 and/or anyother electronic components, such as one or more of the elementsdiscussed in relation to computer device 114 shown in FIG. 1. Controller404 may be embedded or otherwise part of housing 406. Housing 406 may beformed of one or more materials, including elastomeric components andcomprise one or more displays, such as display 408. The display may beconsidered an illuminable portion of the device 400. The display 408 mayinclude a series of individual lighting elements or light members suchas LED lights 410. The lights may be formed in an array and operablyconnected to the controller 404. Device 400 may include an indicatorsystem 412, which may also be considered a portion or component of theoverall display 408. Indicator system 412 can operate and illuminate inconjunction with the display 408 (which may have pixel member 414) orcompletely separate from the display 408. The indicator system 412 mayalso include a plurality of additional lighting elements or lightmembers, which may also take the form of LED lights in an exemplaryembodiment. In certain embodiments, indicator system may provide avisual indication of goals, such as by illuminating a portion oflighting members of indicator system 412 to represent accomplishmenttowards one or more goals. Device 400 may be configured to display dataexpressed in terms of activity points or currency earned by the userbased on the activity of the user, either through display 408 and/orindicator system 412.

A fastening mechanism 416 can be disengaged wherein the device 400 canbe positioned around a wrist or portion of the user 124 and thefastening mechanism 416 can be subsequently placed in an engagedposition. In one embodiment, fastening mechanism 416 may comprise aninterface, including but not limited to a USB port, for operativeinteraction with computer device 114 and/or devices, such as devices 120and/or 112. In certain embodiments, fastening member may comprise one ormore magnets. In one embodiment, fastening member may be devoid ofmoving parts and rely entirely on magnetic forces.

In certain embodiments, device 400 may comprise a sensor assembly (notshown in FIG. 4). The sensor assembly may comprise a plurality ofdifferent sensors, including those disclosed herein and/or known in theart. In an example embodiment, the sensor assembly may comprise orpermit operative connection to any sensor disclosed herein or known inthe art. Device 400 and or its sensor assembly may be configured toreceive data obtained from one or more external sensors.

iii. Apparel and/or Body Location Sensing

Element 130 of FIG. 1 shows an example sensory location which may beassociated with a physical apparatus, such as a sensor, data acquisitionunit, or other device. Yet in other embodiments, it may be a specificlocation of a body portion or region that is monitored, such as via animage capturing device (e.g., image capturing device 118). In certainembodiments, element 130 may comprise a sensor, such that elements 130 aand 130 b may be sensors integrated into apparel, such as athleticclothing. Such sensors may be placed at any desired location of the bodyof user 124. Sensors 130 a/b may communicate (e.g., wirelessly) with oneor more devices (including other sensors) of BAN 102, LAN 104, and/orWAN 106. In certain embodiments, passive sensing surfaces may reflectwaveforms, such as infrared light, emitted by image-capturing device 118and/or sensor 120. In one embodiment, passive sensors located on user's124 apparel may comprise generally spherical structures made of glass orother transparent or translucent surfaces which may reflect waveforms.Different classes of apparel may be utilized in which a given class ofapparel has specific sensors configured to be located proximate to aspecific portion of the user's 124 body when properly worn. For example,golf apparel may include one or more sensors positioned on the apparelin a first configuration and yet soccer apparel may include one or moresensors positioned on apparel in a second configuration.

FIG. 5 shows illustrative locations for sensory input (see, e.g.,sensory locations 130 a-130 o). In this regard, sensors may be physicalsensors located on/in a user's clothing, yet in other embodiments,sensor locations 130 a-130 o may be based upon identification ofrelationships between two moving body parts. For example, sensorlocation 130 a may be determined by identifying motions of user 124 withan image-capturing device, such as image-capturing device 118. Thus, incertain embodiments, a sensor may not physically be located at aspecific location (such as one or more of sensor locations 130 a-130 o),but is configured to sense properties of that location, such as withimage-capturing device 118 or other sensor data gathered from otherlocations. In this regard, the overall shape or portion of a user's bodymay permit identification of certain body parts. Regardless of whetheran image-capturing device is utilized and/or a physical sensor locatedon the user 124, and/or using data from other devices, (such as sensorysystem 302), device assembly 400 and/or any other device or sensordisclosed herein or known in the art is utilized, the sensors may sensea current location of a body part and/or track movement of the bodypart. In one embodiment, sensory data relating to location 130 m may beutilized in a determination of the user's center of gravity (a.k.a,center of mass). For example, relationships between location 130 a andlocation(s) 130 f/130 l with respect to one or more of location(s) 130m-130 o may be utilized to determine if a user's center of gravity hasbeen elevated along the vertical axis (such as during a jump) or if auser is attempting to “fake” a jump by bending and flexing their knees.In one embodiment, sensor location 1306 n may be located at about thesternum of user 124. Likewise, sensor location 130 o may be locatedapproximate to the naval of user 124. In certain embodiments, data fromsensor locations 130 m-130 o may be utilized (alone or in combinationwith other data) to determine the center of gravity for user 124. Infurther embodiments, relationships between multiple sensor locations,such as sensors 130 m-130 o, may be utilized in determining orientationof the user 124 and/or rotational forces, such as twisting of user's 124torso. Further, one or more locations, such as location(s), may beutilized as (or approximate) a center of moment location. For example,in one embodiment, one or more of location(s) 130 m-130 o may serve as apoint for a center of moment location of user 124. In anotherembodiment, one or more locations may serve as a center of moment ofspecific body parts or regions.

Example Metrics Calculations

Aspects of this disclosure relate to systems and methods that may beutilized to calculate one or more activity metrics of an athlete,including but not limited to energy expenditure, speed, distance, pace,power, and/or others. The calculations may be performed in real time,such that the user may obtain real-time feedback during one or moreactivities. In certain embodiments, all calculations for a plurality ofmetrics are be estimated using a same set of attributes, or a sub-set ofattributes from a common group of attributes, and the like. In oneembodiment, a calculation of energy expenditure may be performed on afirst set of attributes and without classifying the activity beingperformed by the athlete, such as being walking, running, playing aspecific sport, or conducting a specific activity. In one embodiment,determinations of energy expenditure are done without any activity typetemplates, such that as the energy expenditure may be calculated fromsensor data and/or derivatives thereof, without classifying the activitytype. For example, energy expenditure may be calculated in accordancewith certain embodiments using the same set of attributes regardless ofwhether the athlete is performing a first activity or a second activity,such as for example, walking or playing soccer.

In certain implementations, calculations of energy expenditurecalculated may be performed using a first set of attributes and anothermetric, such as for example, speed may be determined from the same setof attributes or a subset of the same attributes. In one embodiment,determination of a plurality of metrics may be conducted using aselection of core attributes. In one example, this attribute calculationmay be used to estimate energy expenditure and/or a speed of walkingand/or running of the user. In one example, an energy expenditure and/orspeed of walking and/or running may be estimated using a same set ofattributes, or a sub-set of attributes from a common group ofattributes, and the like.

The systems and methods described herein may compare calculatedattributes from activity data (real-time activity data, and the like) toone or more models wherein the one or more models may not include datacaptured for the activity type that the athlete performed (and may notbe categorized, such as for energy expenditure calculations). In thisway, the one or more models may be agnostic to the specific activitybeing performed by a user. For example, an activity device may receiveinformation from a user performing a basketball activity and at leastone model may not contain any data from basketball activities.

As an example of calculating multiple metrics, systems and methods maybe implemented to determine whether to calculate speed for one or moretime windows of data. Certain aspects of this disclosure relate todeterminations of speed or distance that comprises categorizing athleticdata. As discussed above, however, other aspects relate to calculatingenergy expenditure values without categorizing the athletic data intoactivity types (walking, running, basketball, sprinting, soccer,football, etc.), however, categorizing at least a portion of the samedata utilized to calculate energy expenditure for the calculation ofother metrics, such as for example, speed and/or distance is within thescope of this disclosure. In one implementation, speed (or anothermetric) may be determined from at least a portion of data derived fromthe determination of energy expenditure values. In accordance withcertain embodiments, the attributes are calculated on a single device,such as any device disclosed herein or known in the art. In oneembodiment, the attributes and calculation of the metrics are calculatedon a single device. In one such example, a device configured to be wornon an appendage of a user may be configured to receive sensor data andcalculate the attributes and a plurality of metrics from the attributes.In one embodiment, the single device comprises at least one sensorconfigured capture data utilized to calculate at least one attribute. Inaccordance with certain embodiments, the attributes are calculated fromone or more sensors located on the single device.

Example Energy Expenditure Calculations

One or more of the systems and methods described herein may calculate anestimate of energy expenditure that implements at least one of thecomponents of FIG. 6. In one configuration, a device, such as device112, 126, 128, 130, and/or 400 may capture data associated with one ormore activities being performed by a user, and may include one or moresensors, including, but not limited to: an accelerometer, a gyroscope, alocation-determining device (e.g., GPS), light sensor, temperaturesensor (including ambient temperature and/or body temperature), heartrate monitor, image-capturing sensor, moisture sensor and/orcombinations thereof. This captured activity data may, in turn, be usedto calculate one or more energy expenditure values associated with theuser.

In one implementation, an estimation of the volume of oxygen consumed bya person may be used in the calculation of an effective metabolicequivalent or an estimation of energy expenditure by said person. Forexample, in in one embodiment, a liter of oxygen consumed by a personmay be associated with an energy expenditure of approximately 5kilocalories (5 kcal). Additionally or alternatively, those of ordinaryskill in the art will recognize various alternative methodologies existfor utilizing in calculations of energy expenditure based upon oxygenconsumption by a person.

In one embodiment, oxygen consumption by a person may be measured as avolume of oxygen per unit mass, such as per kilogram (VO₂/kg). In oneimplementation, the systems and methods described herein may utilizevalues, such as stored as computer-executable instructions on one ormore non-transitory computer-readable mediums related to actual and/orestimated oxygen consumption associated with specific activities. Incertain embodiments, the values may be actual data or derived fromactual data collected from one or more individuals performing one ormore specific activities, and otherwise referred to as training data.For example, the systems and methods described herein may utilizetraining data related to oxygen consumption by one or more individualsperforming activities including, among others, playing basketball,playing soccer, playing tennis, walking, jogging, running, andsprinting, sitting, squatting, and/or combinations thereof. Those ofordinary skill in the art will recognize various testing procedures thatmay be utilized to monitor oxygen consumption while an individual isperforming one or more prescribed activities. Additionally, it will bereadily understood to those of ordinary skill in the art that multipleoxygen consumption data points may be collected during an activity.Furthermore, one or more data manipulation processes may be performed onsaid collected data points to calculate, among others, an average oxygenconsumption during a specific activity, and based on a recorded mass(e.g., measured in kilograms, and the like), and/or individual or classspecific information (e.g., sex, weigh, height, age, percent body fat,among others).

In one implementation, training data may be recorded for one or moreindividuals performing one or more specific activities, wherein saidtraining data includes information related to a volume of oxygenconsumed at one or more time points during the one or more specificactivities, and information related to individual and or class-specificinformation (e.g., the mass of the individuals performing the specificactivities). Additionally, training data may include sensor data from adevice, such as device 112, 126, 128, 130, and/or 400. In this way, thetraining day that may store information related to one or more sensoroutputs in addition to information related to one or more volumes ofoxygen consumed during one or more activities. In one implementation thesensor data stored in addition to the oxygen consumption data mayinclude data from one or more of an accelerometer, a gyroscope, alocation-determining device (e.g., GPS), light sensor, temperaturesensor (including ambient temperature and/or body temperature), heartrate monitor, image-capturing sensor, moisture sensor and/orcombinations thereof. For example, training data may include stored datafrom an accelerometer sensor, in addition to information related to avolume of oxygen consumed during an activity, for example, playingsoccer.

Accordingly, the systems and methods described herein may includetraining data that includes one or more calculated attributes associatedwith an activity. Furthermore, the one or more attributes associatedwith an activity may include one or more volumes of oxygen consumed perunit mass of a person at one or more time points during an activity,and/or one or more calculated values based on one or more outputs fromsensors associated with a device monitoring one or more motions of auser during said activity. For example, in one implementation, an outputof an accelerometer sensor may include an acceleration value for each ofthe three axes (x-, y-, and z-axis). Accordingly, in one implementation,a plurality of acceleration values associated with the respective axesto which an accelerometer sensor is sensitive (x-, y-, and z-axis) maybe grouped as a single acceleration data point. In anotherimplementation, acceleration values may be processed by a processor,such as processor 202, associated with a device, such as device 112,126, 128, 130, and/or 400 to calculate one or more attributes.

In one example, one or more data points received from a sensoraggregated into a dataset representative of one or more motions of user.Accordingly, the one or more data points may be processed to representthe data in a way that allows for one or more trends and/or metrics tobe extracted from the data. In one example, acceleration data outputfrom an accelerometer may be processed (transformed) to calculate avector normal (“vector norm”). Additional or alternative transformationsmay be employed to calculate, in one example, a standard deviation ofthe vector normal, a derivative of the vector normal, and/or a FastFourier Transform (FFT) of the vector normal. Those skilled in the artwill appreciate that certain transformations may combine sensor datafrom two or more sensors and/or with other data, such as biographicaldata (e.g., height, age, weight, etc.). In other embodiments,transformation may only utilize data from single sensors, such thatsensor data from multiple sensors is not mixed. Transformations relatedto received motion data points are explained in further detail inrelation to FIGS. 8A and 8B.

In one example, one or more attributes may be calculated from thereceived data, and wherein the attributes may be calculated subsequentto one or more transformation processes be executed on a datasetrepresentative of one or more motions of a user. In this regard,datasets from multiple users may be used as a comparison against a userwhose activity is not within the dataset. This may be done withoutcategorizing the user's activity into an activity type (e.g., walking,running, playing specific sport) and in certain embodiments, the usermay be performing an activity that was not conducted as part ofcollecting the training data within the data used to obtain attributevalues for the models. Examples of attribute calculations are explainedin further detail in relation to FIGS. 9A-9E.

In another example, the systems and methods described herein may beimplemented to estimate one or more metrics from sensor data. Thesemetrics may include, among others, an estimation of energy expenditure,an estimation as to whether a user is running, walking, or performingother activity, and/or an estimation of a speed and a distance (a pace)which a user is moving, and the like. For example, block 601 offlowchart 600 from FIG. 6 shows an example implementation of calculatingenergy expenditure from one or more attributes. Separately, block 603 isdirected to an example implementation for calculating speed, which maybe determined using one or more calculated attributes, which may bederived from the same sensors, from the same data, and/or the sameattributes as the energy expenditure metric.

Accordingly, the systems and methods described herein may utilize datareceived from one or more different sensor types, including, amongothers, an accelerometer, a heart rate sensor, a gyroscope, alocation-determining device (e.g., GPS), light (including non-visiblelight) sensor, temperature sensor (including ambient temperature and/orbody temperature), sleep pattern sensors, image-capturing sensor,moisture sensor, force sensor, compass, angular rate sensor, and/orcombinations thereof.

Furthermore, while the example of attributes associated withacceleration data output from an accelerometer sensor are described,those of ordinary skill will appreciate that other sensors may be used,alone or in combination with other sensors and devices, withoutdeparting from the scope of this disclosure. For example, a heart ratemonitor may be used, wherein the data output from a heart rate monitormay output data representative of a heart rate in units of beats perminute (BPM) or equivalent. Accordingly, one or more transformations maybe performed on outputted heart rate data to interpolate a heart ratesignal between heart rate data points, and allowing for signal dropoutsat certain points. Furthermore, the attributes calculated for sensordata associated with a heart rate monitor, or any other sensor, may bethe same, or may be different to those described above in relation toaccelerometer data.

In another implementation, the systems and methods described herein mayanalyze sensor data from combinations of sensors. For example, a devicemay receive information related to a heart rate of a user from a heartrate monitor, in addition to information related to motion of one ormore appendages of a user (from one or more accelerometers, and thelike). In one example, the device may determine that a user has a heartrate indicative of vigorous exercise, but accelerometer data mayindicate that said user has been at rest for a period of time.Accordingly the device may determine that the user has a sustainedelevated heart rate after a period of activity but is now resting aftersaid activity, and the like.

In one implementation, training data may be used to construct one ormore models, otherwise referred to as experts, or expert models, forpredicting, among others, a volume of oxygen consumption based upon (atleast in part) one or more individual-specific properties such as agender, a mass and/or a height of a user. Accordingly, information fromone or more sensors associated with a device, such as device 112, 126,128, 130, and/or 400, may be used to calculate one or more attributes.In turn, the calculated attributes may be compared to attributesassociated with one or more constructed models, and thereby, used topredict a volume of oxygen being consumed by a user while outputtingmotion signals (sensor output values) corresponding to the calculatedattributes. For example, a user may be performing an activity, such asplaying soccer, while wearing a sensor device on an appendage. Thesensor device, in turn, may output sensor values, which may be processedto calculate one or more attributes. Subsequently, the one or morecalculated attributes may be compared to one or more attributesassociated with one or more models, and an estimation of a volume ofoxygen being consumed by the user while playing soccer may be made.Furthermore, said estimation of a volume of oxygen being consumed may beused to estimate energy expenditure values by the user playing soccer.This process is described in further detail in relation to FIG. 6. Incertain embodiments, all of the sensor data comes from a unitary device.In one example, the unitary device is an appendage-worn device. Incertain configurations, the appendage worn device comprises at least oneof an accelerometer, a location-determining sensor (e.g., GPS) and aheart rate monitor. In another example, the unitary device comprises asensor configured to be placed on or within athletic apparel, such as ashoe. In yet another example, a sensor from at least two differentdevices are utilized to collect the data. In at least one embodiment,the device comprising a sensor utilized to capture data is alsoconfigured to provide an output of energy expenditure. In oneembodiment, the device comprises a display device configured to displayan output relating to energy expenditure. In further embodiments, thedevice may comprise a communication element configured to transmitinformation relating to energy expenditure to a remote device.

In another implementation, one or more attributes may be calculated fromreceived sensor data and used as inputs to one or more walking and/orrunning models for predicting, among others, a speed/a pace of a user.Further details of such an implementation are described in relation toblock 603 of flowchart 600.

FIG. 6 is a flowchart showing an exemplary implementation of attributecalculation. In one example, this attribute calculation may be used toestimate one or more metrics associated with an activity being performedby a user, and wherein said estimation may include an energy expenditurespeed, and/or one or more other metrics. In one example, an energyexpenditure and/or speed of walking and/or running may be estimatedusing a same set of attributes, or a sub-set of attributes from a commongroup of attributes, and the like.

Information related to the movement of the user may be outputted as oneor more data signals from one or more sensors associated with one ormore sensor devices monitoring the user. In one implementation, FIG. 6represents one or more processes carried out by at least one processor,such as processor unit 202, which may be associated with a sensordevice, such as, among others, device 112, 126, 128, 130, and/or 400.Accordingly, devices may not directly monitor a volume of oxygen beingconsumed by a user during an activity. In one implementation, one ormore sensor devices may monitor one or more motions associated with oneor more activities being performed by a user. Furthermore, in onearrangement, received activity data may be correlated with observedoxygen consumption values for activities that may exhibit certainattributes, and associated with one or more oxygen consumption models.

One or more embodiments receive sensor data from one or more sensors(see, e.g., block 602). In certain embodiments, the sensor data may beassociated with a device worn by a user. In one example, and aspreviously described, said device may be, among others, device 112, 126,128, 130, and/or 400. Accordingly, the sensor data may be received by aprocessor, such as processor 202 from FIG. 2, and may be received fromone or more sensors described herein and/or known in the art. In oneimplementation, sensor data may be received at block 602 from anaccelerometer at a frequency of, among others, 25 Hz. Additionally oralternatively, sensor data may be received from the sensor, such asaccelerometer, in windows of between 5.0 and 5.5 seconds. In oneembodiment, the window (or time frame) may be about 5.12 seconds inlength. A window may be a period of time during which sensor data isrecorded for one or more motions of a user associated with one or moreactivities being performed by a user. In one implementation, a samplewindow may include 128 samples (data points) of sensor data, wherein asample of sensor data may include a value for each of three orthogonalaxes of an accelerometer (x-axis, y-axis, and z-axis), and/or a vectornormal value. In yet another implementation, sensor data received from,in one implementation, an accelerometer, may be received in windows thatdo not overlap (e.g., 5.12 second-length groups of sensor data eachcontaining 128 samples, and received singly, rather than simultaneously,and/or discrete from each other). However, in alternative embodiments,those of ordinary skill will readily understand that the systems andmethods described herein may be employed with any frequency of operationof an accelerometer, with a window length measuring any length of time,and using any number of samples of sensor data from within a givenwindow. Data may be validated as it is received, such as for, forexample, at block 604. Data validation may include, among others, aperson of one or more values of received sensor data to one or morethreshold values, and the like. Various examples of example datavalidation embodiments are described in further detail in relation toFIG. 7.

Further aspects of this disclosure relate to calculating one or moreattributes from the data (see, e.g., block 606). Calculation of one ormore attributes may occur after validation protocols, such as thosedescribed herein, including in relation to FIG. 7. In oneimplementation, one or more attributes may be calculated for one or moreof the received samples in a sample window (e.g., the 128 sample windowdescribed above). Attribute calculation may occur in real-time as thedata is collected. Various example embodiments that may be implementedto calculate one or more attributes are described in greater detail inrelation to FIGS. 9A-9E.

Further embodiments may compare one or more calculated attributesassociated with data received from one or more sensors, and indicativeof one or more activities being performed by a user, to one or moreattributes associated with one or more models. In one example, one ormore attributes may be compared to one or more models (see e.g., block608). For example, for calculations of energy expenditure, one or moreattributes may be compared to oxygen consumption models. In anotherexample, attributes may be used as inputs to one or more of, amongothers, models for estimation of a number of steps (during walking),strides (during running), or other movements by a user, and/or modelsfor estimation of speed and distance (pace) of a user (see e.g., block603 of flowchart 600, FIG. 15A-15C, and/or FIG. 16). Furthermore, aswill be apparent from FIG. 6, one or more calculated attributes may beused as inputs to one or more models such that, in one example, a modelfor estimation of an energy expenditure may be executed separately froma model for calculation of a step rate, a walking speed, and/or arunning speed, and the like. As previously described, one or more modelsmay be stored in a memory, such as memory 212, and the like, andassociated with a device, including a sensor device.

In one implementation, a model may include information (e.g. trainingdata) collected during one or more user's performance conducting one ormore activities and, in one example, predicted oxygen consumption. Themodels may include training data from activities that, despite beingdifferent from the activity that an athlete is performing, may havesimilar relationships between attributes. As such, the models may serveas accurate predictors of oxygen consumption. Accordingly, a model mayinclude training data associated with one or more different activities.For example, a model may include training data received from one ormonitoring processes associated with, among others, playing soccer andplaying basketball. In this way, oxygen consumption data associated withcertain movements of soccer and basketball activity data may be similar(within one or more predetermined numerical ranges for different periodsduring the activities, and the like).

In another implementation, a first oxygen consumption model may comprisedata from a same one or more users as those one or more users' data usedin a second oxygen consumption model. In another configuration, a firstmodel and a second model may use a same one or more users' data. In yetanother configuration, a data associated with a model may have beencaptured from a same one or more users during a single data collectionperiod, or from multiple collection periods across a same or a differentday, and the like. In one implementation, a first model may beassociated with data from a first group of one or more sensors and asecond model may be associated with a second group of one or moresensors, and wherein the first group may share one or more, or none ofthe same sensor types. In one implementation, the systems and methodsdescribed herein may compare calculated attributes from activity data(real-time activity data, and the like) to one or more models whereinthe one or more models may not include data captured for that activitytype. In this way the one or more models may be agnostic to the specificactivity being performed by a user. For example, an activity device mayreceive information from a user performing a basketball activity. Inresponse, the device may process the received basketball activity data(such as, for example, block 606 of flowchart 600), and comparecalculated attributes to one or more models (such as, for example, block608). In one implementation, the one or more models may or may notcomprise data related to basketball. In this way, the computed one ormore attributes for received sensor data may correspond to one or moremodels, and wherein the models need not comprise training data relatedto the specific activity being performed by a user.

In one configuration, a plurality of models, each with their ownattributes, which may or may not overlap attributes of other models, maybe utilized. In one example implementation, each model may be associatedwith 20 attributes. In another configuration, the systems and methodsdescribed herein may store 5, 10, 15 or 21 attributes for each model.However it will be readily understood to those of ordinary skill thatthe systems and methods described herein may store any number ofattributes in association with each model, and in certain embodiments afirst number of attributes stored in association with a first model maydiffer from a second number of attributes stored in association with asecond model. Furthermore, the one or more attributes stored inassociation with a model may be alternatively referred to as weights, ormay be used to calculate related weighting values that may be used tocompare a model to those attributes calculated from sensor data receivedfrom a device. Accordingly, in one implementation, the number ofattributes calculated from data received from a sensor device (ormultiple sensor devices) collecting movement data of a user may be equalto a number of attributes, or alternatively, a number of weightsassociated with one or more stored oxygen consumption models.

One or more aspects may calculate a probability that a first model willprovide the most accurate estimation of oxygen consumption from thetotal number of stored one or more models. For example, a probabilitythat a specific model, from a group of different (e.g., 16) models ismost likely to provide the most accurate output of oxygen consumptionmay be calculated. The calculations may be performed, for example, aspart of block 608, and be based on one or more attributes calculatedfrom received sensor data indicative of an activity being performed by auser. In one implementation, a level of closeness between attributescalculated from received sensor data, and those attributes, oralternatively, those weights, associated with one or more of the storedoxygen consumption models may be calculated, such as for example as partof block 608. In one implementation, the systems and methods describedherein may execute one or more processes to compare input attributeswith corresponding weights associated with a stored model (expert). Inone example, block 608 may calculate a probability for each storedmodel. Accordingly, said probability may represent a likelihood that thecalculated one or more attributes are a best-fit for a given model. Forexample, a probability may be calculated for each of 16 stored models,and the like. The highest probability value, from the 16 calculatedprobabilities, indicates the model with the best-fit for the calculatedattributes, and the like. Further details related to a comparison ofcalculated attributes to stored models, and subsequent selection of abest-fit model for those calculated attributes, are explained inrelation to FIG. 14.

In one example, block 610 represents one or more processes to select amodel with a best-match, or best-fit to those calculated attributes fromblock 608. As previously described, said best-fit model representsstored training data that most closely approximates the data receivedfrom a sensor device monitoring an activity of the user. In oneimplementation, the best-fit model may be the model corresponding to acalculated probability value that is closest to a value of 1.0. Inanother implementation, block 610 may select two or more models. Forexample, models fitting within a predefined deviation, variation, and/orthreshold may be selected (referred to as a hybrid-model strategy).

As shown by illustrative block 612, one or more embodiments may estimatean output value from a selected model (expert). In one example, anoutput may be in estimation of a volume of oxygen consumption as aresult of one or more activities being performed by a user. Accordingly,in one example, block 612 may correlate an activity being performed by auser with an estimated oxygen consumption value, and based on a selectedmodel (e.g., a best-fit model) that most closely matches attributevalues calculated from sensor data. In one implementation, a model maystore one or more estimates of oxygen consumption, wherein the one ormore estimates of oxygen consumption may be stored based on at least oneindividual-specific value, such as a gender, a weight and/or a height ofa user. Accordingly, and based upon the one or more attributesassociated with the sensor data received from a sensor device, block 612may return an estimate of a volume of oxygen consumption by the userbased upon an activity carried out by the user.

Block 614 may be implemented to estimate a motion metric associated witha model output value. In one example, a motion metric may be an energyexpenditure value, which may be estimated using an oxygen consumptionvalue. As previously described, one or more methods may be utilized tocalculate an energy expenditure from an oxygen consumption value. In oneexample, an estimated energy expenditure of 5 kcal is associated with anoxygen consumption of 1 L by a user. However those of ordinary skill inthe art will recognize various other formulae for calculating energyexpenditure based upon a value of oxygen consumption, and using one ormore individual-specific values (e.g., a height and a weight of a user).

In another implementation, one or more attributes calculated (including,e.g., at block 606) may be used in a determination of whether sensordata is indicative of walking, running, or another activity beingperformed by a user, and additionally or alternatively, the speed atwhich the user is walking or running, and the like. Accordingly, one ormore attributes calculated at block 606 may be used as inputs to block603. In particular, one or more attributes may be communicated fromblock 606 to decision block 616. At block 616, one or more processes maybe executed to determine whether a user is running or walking, orperforming another activity. Further details related to these one ormore processes are described in relation to FIGS. 15A-15C. If it isdetermined that a user is performing an activity other than running orwalking, flowchart proceeds from block 616 to 618. Accordingly, for auser performing activity other than running or walking, no processes areexecuted to define the speed at which the user is traveling, and thelike. If it is determined, at decision 616, that's the user is runningor walking, decision 620 may be implemented to determine whether theactivity is awalking or running. Example embodiments for selecting anactivity, such as running or walking, are provided herein, including inrelation to In one embodiment, if tt is determined that a user isrunning, one or more attributes may be communicated to a running model,such as to determine speed (e.g., see block 622). If, however, it isdetermined that a user is walking, certain embodiments may communicateone or more attributes to a walking model, such as to determined speed(e.g., block 624).

FIG. 7 depicts a flowchart 700 showing an example implementation thatmay be utilized in the verification of data received from one or moresensors. In one example, flowchart 700, either in whole or in part, maybe implemented as part of executing verification implementations ofblock 604 from FIG. 6. Block 702 may represent one or more processes forreceiving data from one or more sensors. As previously described, datamay be received from sensors of different types, including, but notlimited to, accelerometers, heart rate monitors, gyroscopes,location-determining device (e.g., GPS), light (including non-visiblelight) sensor, temperature sensor (including ambient temperature and/orbody temperature), sleep pattern sensors, image-capturing sensor,moisture sensor, force sensor, compass, angular rate sensor, and/orcombinations thereof. Data may be received, at block 702, as one or moredata points, or samples. Furthermore, data may be received as part of acontinuous, or in intermittent data stream. Additionally, and as will bereadily understood to those of ordinary skill in the art, data receivedat block 702 may be from a single sensor, or multiple sensors, and maybe received as a serial input, or as a parallel import (simultaneously).Block 702 may encompass portions of block 602 in certain embodiments.

Block 704 may include one or more processes to identify a source ofreceived data.

Accordingly, block 704 may identify a sensor type from which data isreceived. In one implementation, the information contained within a datapoint (a sample) may include an identification of a sensor type,sub-type, model and/or specific instance of a sensor from which the datawas generated and communicated. For example, block 704 may identifyreceived data that has been received from an accelerometer, and thelike. In another example, block 706 of flowchart 700 may identify thereceived data as being from one or more sensors. If one or more receiveddata points are received from separate sensors, f the data stream may beparsed (e.g., block 708). In one example, block 708 may be implementedto separate received data points based upon a sensor type from which thedata has been received, and the like.

A value of a received data point may be compared to one or morethreshold values (e.g., 710). Accordingly, block 710 may validate areceived data point. In this way, a data point may be identified asrepresenting a motion of a user, and distinguished from one or morevalues representing noise, and the like. For example, for anaccelerometer data point, a threshold value may be, among others, 10% ofa maximum output value from an accelerometer sensor. Accordingly, if areceived data point from an accelerometer has a numerical value (in oneexample, an absolute numerical value, and the like), of less than 10% ofthe maximum output value from the accelerometer sensor, the data pointmay be identified as noise, and disregarded. Correspondingly, if thereceived data point from an accelerometer has a numerical value ofgreater than 10% of the maximum output value from the accelerometersensor, the data point is it identified as a valid acceleration.

Those of ordinary skill in the art will recognize that a threshold valuemay take any numerical value, or otherwise, that is appropriate for aparticular sensor type, and based upon, among others, an output range ofnumerical values from a particular sensor type. Furthermore, theexemplary 10% threshold associated with an accelerometer mayadditionally or alternatively be a 5% threshold, a 15% threshold, a 20%threshold, or 25% threshold, or any other numerical value, and the like.Furthermore, there may be more than one threshold associated with aspecific sensor type. For example, a sensor may have two or morethresholds associated with data outputs. A first threshold maydistinguish between noise and true acceleration data, and may be, forexample, a 10% threshold. Furthermore, a second threshold maydistinguish between true acceleration data and saturated data, and maybe, for example, a 95% threshold, and the like.

Data from multiple sensors or inputs may be validated, such as at block710. As one example, one or more data points from a heart rate sensor,wherein a heart rate sensor data point may be compared to one or morethresholds. In one example, if a received heart rate is between 20 and220 bpm, the heart rate data point is considered proper, and maybesubsequently communicated to a data buffer, such that the bufferdescribed in relation to block 712, and the like. In another example, avalidation of heart rate sensor data may execute one or more processesto check that a number of heart rate data points are within a one ormore predetermined beats per minute thresholds while a buffer is beingpopulated with acceleration data.

One or more data points may be validated as being representative ofuseful data, such as for example at block 710, wherein useful data maybe representative of one or more activities or performances beingcarried out by a user. Accordingly, subsequent to validation of one ormore data points, the validated data points may be stored (temporarilyor otherwise) in a buffer (see, e.g., block 712). Those of ordinaryskill in the art will understand that's various types of buffers may beused without departing from the scope of this disclosure. Accordingly,in one example, a buffer may be a dedicated hardware chip that includeselements configured for storage of digital information. A buffer may beimplemented in a form of persistent memory, such as a hard disk drive(HDD), a solid state drive (SDD), an optical disk, and the like.Additionally or alternatively, buffer may be implemented in a form ofvolatile memory, such as, among others random access memory (RAM), andthe like.

Certain embodiments may be configured to add one or more data points toa buffer, wherein said buffer may, in one example, store 128 data points(samples) before communicating the contents of the buffer to atransformation module. Those of ordinary skill in the art will recognizethat, in one example, fewer than 128 data points may be stored in thebuffer. For example, the buffer may store 64 samples, 32 samples, 16samples, and the like. Similarly, those of ordinary skill will recognizethat, in another example, greater than 128 data points may be stored inthe buffer, and without departing from the scope of the disclosuredescribed herein. Additionally or alternatively, the number of samplesand/or data points stored in a buffer may be dependent upon the sensortype from which the data is received. For example, data received from anaccelerometer may occupy more storage space than data received from aheart rate monitor, and the like. Furthermore, in one example, atransformation module may include one or more processes for manipulatingand/or identifying features of received data, and the like.

In one implementation, a time stamp may be associated with a data pointas it is added to the buffer. In another implementation, a timestamp maybe associated with a validated data point independently of a buffer, andthe like.

Certain embodiments may confirm whether a threshold number of datapoints are within a buffer, or series of buffers (see, e.g., block 714).In this way, block 714 may represent one or more processes to identifythe number of stored data points and a buffer, and compare this storednumber of data points to a threshold number of data points beforeflowchart 700 proceeds to block 716. For example, block 714 may checkthe number of data points stored in a buffer, and compare this number ofstored data points to a threshold number of 128 data points, and thelike. In one example, once block 714 identifies 128 samples stored in abuffer, flowchart 700 proceeds to block 716, wherein the 128 samples arecommunicated to a data transformation module. If, however, block 714determines that the number of samples stored in a buffer is less than athreshold number of, for example, 128 samples, flowchart 700 proceeds toblock 702 for processing of additional data points, and the like. Thoseof ordinary skill will recognize that block 714 may be omitted fromflowchart 700, without departing from the scope of the disclosure, andone or more data points may be communicated to a transformation modulecontinuously, and without having received a threshold number of datapoints, and the like.

In accordance with one embodiment, a threshold of 128 data points mayrepresent a window of data, wherein a window of data, which if collectedat a frequency of 25 Hz would result in a length of time of about 5.12seconds. Accordingly, a sample window may comprise a number of samplesother than 128, sampled at any given frequency, and for any length oftime, and the like. Furthermore, a transformation module may be one ormore processes configured to execute on data received from one or moresensors. The one or more transformation processes may aggregate datainto a dataset, wherein a dataset may be a group of data pointsrepresentative of a motion and/or an activity of a user, and the like.Accordingly, data transformation processes are described in furtherdetail in relation to FIGS. 8A and 8B.

FIG. 8A is a flowchart diagram 800 that includes one or more exemplaryprocesses for transforming received sensor data. In one example,validated data, which may be validated in accordance with one or moreteachings set forth in FIG. 7, may be received (e.g., block 802). In oneconfiguration, flowchart 800 may represent one or more processes totransform data received from an accelerometer sensor. Accordingly, adata point output from an accelerometer may include a data valueassociated with one or more of three orthogonal axes (x-axis, y-axis,z-axis), and the like. In one specific implementation, an accelerometermay output a numerical value representative of an acceleration along asingle axis (a single axis selected from one or more of an x-axis,y-axis, or z-axis), and on a scale of 0 to 1024. Those of ordinary skillin the art will recognize that accelerometers manufactured by differentcompanies, or indeed, sensors of different types, will output numericalvalues different to those described herein. In this specific example, anoutput value of 1 to 512 may represent an acceleration in a negativedirection along an axis. Correspondingly, an output value of 513 to 1024may represent an acceleration in a positive direction along said sameaxis. Block 804 of flowchart 800 may execute one or more processes tozero-center one or more data points output from an accelerometer, andthe like. Zero-centering data, in one implementation, may be used totransform data values associated with one or more data points onaccelerometer such that acceleration of 0 m/s², is outputted withnumerical value of zero, and the like. In one example, one or moreprocesses to zero-centered data may include subtracting 512 from anoutput value from an accelerometer. As such, an acceleration along anegative direction of an axis may be represented by a negative valuefrom −1 to −512, and an acceleration along a positive direction of anaxis may be represented by a positive value of +1 to +512, and the like.

In one example, data points associated with an accelerometer may includeone or more numerical values representing one or more axes to which theaccelerometer is sensitive to acceleration. For example, anaccelerometer may output three numerical values associated with each ofthree orthogonal axes (x-axis, y-axis, and z-axis). In one example, thephysical orientation of an accelerometer circuit and/or chip in adevice, such as device 112, 126, 128, 130, and/or 400, may control theoutput values from the accelerometer. For example, an accelerometerpositioned within a device in a first orientation may outputacceleration values primarily along an x-axis during a first activity.However, the same accelerometer positioned within a device in a secondorientation may output acceleration values primarily along a y-axisduring said same first activity, and the like. As such, one or moreprocesses may be dependent upon a physical orientation of accelerometersensor within the device, and wherein the orientation of the sumaccelerometer sensor may differ between two devices. In oneimplementation, however, block 806 may execute one or more processes tosource data output from an accelerometer sensor based on a magnitude ofthe output values, and the like. For example, for each data pointreceived from an accelerometer, one or more processes may execute toidentify the axis, from a group of, in one implementation, three axes(x-axis, y-axis, and z-axis) with the highest value of acceleration. Inone implementation the highest value of acceleration may be an absolutevalue, and the like. Accordingly, the axis corresponding to the highestvalue of acceleration may be re-labeled/re-ordered as the x-axis.Furthermore, the axis corresponding to the second highest value ofacceleration may be re-labeled/re-ordered as the y-axis. Additionally,the axis corresponding to the third-highest value of acceleration may bere-labeled/re-ordered as the z-axis, and the like. In this way,re-ordering the axes by magnitude allows one or more subsequent datamanipulation processes to be agnostic to the physical orientation of anaccelerometer sensor within a device, such as device 112, 126, 128, 130,and/or 400, and the like.

In one example, block 808 may transform one or more data points receivedfrom an accelerometer sensor by calculating a vector normal of thereceived data. Those of ordinary skill in the art will recognize one ormore processes to calculate a vector normal of a data point comprisingthree values representative of each of three orthogonal axes to which anaccelerometer is sensitive. In one example, a vector normal may becalculated as a square roots of a sum of squares according to equation 1below:Vector normal=SQRT((x_i)²+(y_i)²+(z_i)²).  (Equation 1)

Given equation 1 above, those of ordinary skill in the art willrecognize that x_i is a value of acceleration along an x-axis, y_i is avalue of acceleration along a y-aixs, and z_i is a value of accelerationalong a z-axis, and the like.

Further transformations may be carried out on data received from asensor, wherein said data may be received from an accelerometer sensor,and may be validated prior to transformation. In one example, block 810represents a transformation to calculate a derivative of a vectornormal. Accordingly, those of ordinary skill in the art will recognizevarious systems and methods for calculating a derivative, such as aderivative of a vector normal, and the like. In another example, block812 represents a transformation to calculate a Fast Fourier Transform(FFT) of a vector normal. Similarly, those of ordinary skill in the artwill recognize various systems and methods for calculating a FFT of datato represents a data in a frequency domain: the like.

Block 814 of flowchart 800 represents one or more processes tocommunicate transform data to an attribute calculation module. Thetransformed data may be represented as one or more of zero-centered datasorted by magnitude, vector normal data, derivative of vector normaldata, and/or a FFT representation of vector normal data, among others.Additionally, an attribute calculation module may comprise one or moreprocesses for extracting information from a dataset, wherein said theextracted information may characterize one or more motions and/oractivities being performed by a user. In one example said extractedinformation may be referred to as one or more attributes of the data,and the like.

FIG. 8B depicts a flowchart 820, wherein flowchart 820 may comprise oneor more processes for transforming received data, and the like. Block822 of flowchart 820, in one example, may represent received ofvalidated data. Data may be validated in accordance with one or moreteachings herein, including the teachings in relation to, for example,to FIG. 7. In one example, the received validated data may berepresentative of one or more hearts rate data points received from aheart rate monitor, and the like.

In one example, block 824 may represent one or more processes tointerpolate between received heart rate data points. For example, therate at which data points representative of a heart rate of a user arereceived may be, among others, 1 Hz. In one implementation, it maydesirable to receive at least one or more than one heart rate data pointper unit time (such as per second). Accordingly, in one implementation,interpolation between received heart rate data points may be employed togenerate data points corresponding to a rate higher than 1 Hz, and thelike. Subsequently, and as represented by block 826, one or moreprocesses may be executed to communicate interpolated heart rate datapoints to an attribute calculation module, and the like.

FIG. 9A depicts an exemplary flowchart 900 that may include one or moreprocesses for calculation of one or more attributes from received sensordata. In one example, data received from one or more sensors may bevalidated, and transformed prior to receipt by block 902. In anotherexample, however, one or more data points may be communicated directlyto block 902 without validation or transformation, and the like.

In one example, FIG. 9A may represent one or more processes to calculateone or more attributes from one or more received accelerometer datapoints. For example, block 904 of flowchart 900 may execute one or moreprocesses to identify an accelerometer axis indicating a greatest amountof acceleration. In one example, and as a result of one or more sortingprocesses executed at block 806 of flowchart 800, the axis correspondingto a greatest amount of acceleration will be an x-axis, and the like.Accordingly, block 904 may identify those data values associated with anx-axis. However, those of ordinary skill in the art will understand thatthe acceleration values associated with highest acceleration may bere-ordered using one or more alternative methods, and such that an axisrepresenting a highest amend of acceleration may differ between datapoints, or may be re-ordered to be a y-axis or a z-axis, and the like.

As previously described, an attribute may generally be a calculatedvalue representing one or more motions of a user, or parts thereof, andwhich may be used to subsequently predict an output from a model.Furthermore, a model may be used to, among others, predict oxygenconsumption by a user during an activity, and the like. A plurality ofdifferent attributes may be calculated from a single data point receivedfrom a sensor, or a group of data points/group of samples, otherwisereferred to as a dataset, and the like. In one example, a group ofattributes used to estimates, among others oxygen consumption by a usermay include, a vector normal oven acceleration data point, a derivativeof vector normal of an acceleration data point, and/or a Fast FourierTransform of vector normal of an acceleration data point, as describedin relation to FIG. 8A.

In another example, further specific attributes may be calculated fromdata received from an accelerometer. For example, an attribute may becalculated as a value representing one or more motions of a user, andassociated with an acceleration signal from an accelerometer axisindicating a greatest amount of acceleration. In one example, anattribute calculated at block 906 may include a statistical valuerelated to a data point received from an axis indicating the greatestamount acceleration, such as a mean value, and the like.

Block 920 represents one or more processes to calculate one or moreattributes to an expert selection module. Accordingly, an expertselection module may comprise one or more processes to compare one ormore calculated attributes to one or more expert models, wherein anexpert model may be used to predict/estimates an output associated withan activity being performed by a user. In one example, such an outputmay be a volume of oxygen consumption by a user, and the like.

FIG. 9B includes a flowchart 930, wherein flowchart 930 may include oneor more processes to calculate one or more attributes from receivedsensor data. Similar to FIG. 9A, block 932 of flowchart 930 representsone or more processes to receive transformed sensor data. Accordingly,data transformation is described in further detail in relation to FIG.8A and FIG. 8B.

Block 934 of flowchart 930 may identify accelerometer axis indicating asecond largest amount of acceleration. In one example, and subsequent toone or more transformation processes including one or more axisre-ordering processes, the axis indicating a second largest amount ofacceleration may be a y-axis, and the like.

Block 938 may represent one or more processes to calculate an attributefrom the received sensor data. In one example, a calculated attributemay be a statistical value. Furthermore, specific examples of attributesare described in relation to FIG. 17B, which may be similar to theprocesses that may be executed in relation to FIG. 9B, and the like.Subsequently, a calculated attribute may be communicated to an expertselection module, as described in relation to block 942.

FIG. 9C depicts a flowchart 950 which may include one or more processesfor calculating one or more attributes of received data, wherein thereceived data may be one or more data points from accelerometer, and thelike. Accordingly, block 952 represents one or more processes to receivetransformed data, wherein data transformations are described in relationto FIG. 8A and FIG. 8B. Alternatively, those of ordinary skill willrecognize that the received data may not be transformed, such thatreceived data at block 952 may, alternatively, be raw data received fromone or more sensors.

Block 954 may represent one or more processes to identify dataassociated with an axis of accelerometer indicating a third largestamount of acceleration. In one example, and as described in relation toblock 806 of FIG. 8A, the axis associated with a third largest amount ofacceleration may be a z-axis.

Block 956 of flowchart 950 represents one or more processes to calculatean attribute from the received sensor data. In a similar manner to FIG.9A and FIG. 9B, a calculated attribute may be a statistical value, suchas a mean value of received data, and the like. Additional detailsrelated to population of one or more attributes are given in relation toFIG. 17C, which includes features similar to those described in relationto FIG. 9C.

Block 968 may execute one or more processes to communicate one or morecalculated attributes to an expert selection module, wherein said expertselection module is described in relation to FIG. 14.

FIG. 9D is a flowchart diagram 970 which may represent one or moreprocesses to calculate one or more attributes associated with receivedsensor data. For example, block 972 may receive transformed sensor datafrom accelerometer. Accordingly, block 974 may group those data pointsfrom all axes associated with the accelerometer. In one example, anaccelerometer may output data related to one, two, or three orthogonalaxes (x-axis, y-axis, and/or z-axis). Block 978 represents one or moreprocesses to calculate an attribute from the received sensor data. Assuch, a calculated attribute may be a statistical value, such as a meanvalue of the received sensor data, and the like. Further details relatedto attribute calculation are given in relation to FIG. 17D, whichincludes elements similar to those described in relation to FIG. 9D.

Subsequent to calculation of one or more attributes, and as representedby block 982 of flowchart 970, one or more attributes may becommunicated to an expert selection module. An expert selection modulemay be one or more processes associated with selecting a best-fit modelassociated with calculated attributes, and as described in furtherdetail in relation to FIG. 14.

FIG. 9E is a flowchart diagram representing one or more processes tocalculate attributes associated with received sensor data. In oneexample, flowchart 986 represents one or more attribute calculationprocesses associated with data received from an accelerometer, and thelike. Accordingly, block 988 represents one or more processes to receivetransformed accelerometer data. In one implementation, and as describedin relation to FIG. 8A, accelerometer data may be transformed bycalculation of a vector normal of said data, and the like. In oneexample, block 990 identifies a calculated vector normal ofaccelerometer data. From said calculated vector normal data, one or moreattributes may be identified.

In one example, block 992 of flowchart 986 represents one or moreprocesses to calculate an attribute from the received sensor data, andin particular, the vector normal data. In one example, a calculatedattribute may be, among many others, a mean value of the vector normaldata, and the like. Additional examples of attributes related to vectornormal data are described in relation to FIG. 17E.

Subsequently, block 999 may communicate data to an expert selectionmodule. An example expert selection module is described in furtherdetail herein, including in relation FIG. 14.

Although aspects of this disclosure relate to systems and methodsconfigured to calculate energy expenditure without determinationswhether a user is conducting an activity, such as running or walking,certain embodiments may determine step count, which may (either directlyor indirectly) be used to calculate one or more attributes. Inaccordance with certain embodiments, systems and methods may beimplemented to conduct frequency estimation and setting up frequencysearch ranges to locate peaks. In one embodiment, peak locating systemsand methods may be utilized on data within a buffer, such as theanalysis buffer. Yet in other embodiments, other data may be utilized,alone or in combination with data within the analysis buffer. FIG. 10provides a flowchart 1000 showing one example process for estimatingfrequency. Those skilled in the art will appreciate that FIG. 10 ismerely one of many embodiments that may be utilized in accordance withvarious implementations. One or more systems and methods may beimplemented to identify a sub-group (or sub-groups) of peaks within thefrequency data to utilize in the determinations of step quantification.In one embodiment, a FFT is performed and peaks in the FFT spectrum maybe identified, such as with the thresholds and/or derivative around thepeak. The performance of the FFT may occur before, during, or afterinitiating frequency estimation processes, such as one or more processesdescribed in relation to FIG. 10. In further embodiments, the FFT mayutilize one or more threshold and derivatives derived from one or morecomponents of flowchart 1000.

In one embodiment, a specific peak (or peaks) within the data (such asfor example, data obtained within a buffer and/or data obtained during atime frame) may be utilized. In one embodiment, “bounce peaks,” “armswing peaks,” and/or other peaks may be identified. For example, manyusers “bounce” upon landing their feet when running. This bounce mayprovide frequency peaks within the data. Other peaks (and/or valleys)may be present within the sensor data. For example, many users oftenswing their arms in a predictable manner during running and/or walkingto provide “arm swing peaks”. For example, arms usually swing along ananterior/posterior axis (e.g., front to back). This frequency may beabout half the frequency of the “bounce peaks”. These peaks, however,may each vary independently, based upon, for example, the individual,the type of motion, the terrain, and/or a combination thereof.

In certain embodiments, thresholds for a function to detect frequenciesmay be determined or retrieved (e.g., block 1002). One or more systemsor methods for determining identification criteria for locating peaksmay estimate frequency of the data points. For example, an average (suchas for example, a mean value) and/or a standard deviation (or variance)may be obtained. Such data may be utilized to determine “peaks” and“valleys” (e.g., the high and low values within the data), which may bequantified. Such data may be used in determinations of dynamicthresholds and/or derivative around the peak. In one embodiment,weighted averages, such as one or two-pass weighted moving averages ofdata in the buffer may be utilized in any determinations. In furtherembodiments, raw sensor data (e.g., accelerometer signals) may also beused, either alone or in combination with other attributes, such asderivatives of the data.

In one embodiment, a 1-pass weighted moving average, a 2-pass weightedaverage and raw data are each used. In another embodiment, only the2-pass weighted moving average may be used. In one embodiment, the meanand standard deviation of the derivatives are calculated and may be usedas threshold levels. In one embodiment, one or more processes may beutilized to obtain thresholds. For example, a first method may beutilized to locate peaks within a fixed range. Yet in certainembodiments, a second method may be utilized to determine identificationcriteria for locating peaks. In certain implementations, the first,second or additional methods may be implemented based, at least in part,on battery life. For example, the second method may require additionalprocessing power, and therefore, may not be utilized upon receiving anindication that the battery life was decreased below a set point, and/oris declining at a rate above a threshold.

At block 1004, a step frequency may be determined for the specificbuffer (or groups of buffers). In certain embodiments, a meanacceleration of the buffer may be used to create a distinct narrowsearch range of the data (e.g., frequencies). For example, FIG. 11 showsgraph 1100 showing the mean acceleration (expressed in meters per secondsquared “m/s²”) along x-axis 1102 and the frequency in Hertz (Hz) of thedual foot step frequency along y-axis 1104. Area 06 shows the detectionarea, which may be constrained by boundary lines 1108 a-1108 d. One ormore of boundary lines 1108 may be based, at least in part, on thethresholds calculated at block 1002. Thus, if accelerations generate afrequency outside the frequency range that the accelerometry predicts(e.g., outside of boundary lines 1108 a-1108 d), then certain systemsand methods may not utilize at least a portion of this data. This may beutilized to ensure data considered to be random noise (e.g. data withdifferent frequency content but similar acceleration magnitudes). In oneembodiment, a mean frequency may be approximated. In one embodiment, themean frequency of sensor data measured along one or more axes may becalculated. For example, sensor data collected from one or moreaccelerometers may be used to determine the mean frequency along one ormore of the x, y and z axes. For example, arm swing data may comprisecomponents along each of the three axes, and thus measured. In oneembodiment, the mean frequency for multiple axes may be approximated byexamining the number of peaks and/or valleys in the data.

In accordance with certain embodiments, sensor data may be utilized todetermine step count regardless of whether the step counts areresponsive to walking and/or running. In this regard, it may beadvantageous to retain sensor data comprising non-walking and/ornon-walking data, rather determinations of walking and or running may bedetermined from attributes.). For discussion purposes of example sensorreadings, however, FIG. 11 is provided to show signals within the rangeof 0.5-2.4 Hz (located along y-axis 1104) which using other categorizingtechniques may be considered as indicative of walking (see, e.g.,samples designated by 1110). In another embodiment, signals within therange of 2.4 to 5 Hz may be considered as indicative of running in othercategorizing techniques. For example, data points 1112 may indicate theathlete is running an 8-minute mile and data point 1114 may indicatethat the athlete is running a 5.5 minute mile. Further, in certainembodiments, arm swing data may be utilized to determine the dual footstep frequency (see axis 1104). For example, if a wrist-worn device isconfigured to measure arm swings, such data may be interpreted as thesingle foot frequency. In this regard, single foot frequency data pointsdesignated by element 1116 may correspond to half the value (withrespect to the y-axis 1104) to data points 1110. In one embodiment,therefore, the value of the single foot frequency may be doubled toarrive at the dual foot step frequency value. Those skilled in the artwill appreciate that graph 1100 is no required to be generated ordisplayed, but rather is illustrated herein to demonstrate aspects ofthis disclosure. Decision 1006 may be implemented to determine whetherto adjust the estimated step frequency. In one embodiment, decision 1006may consider whether steps were counted (or the frequency of steps) in aprevious buffer, such as a preceding non-overlapping buffer. Forexample, decision 1006 may determine whether a successful FFT locatedsteps in previous data. As would be appreciated in the art there may besituations in which the data (e.g., frequencies) change, however, theuser may still be conducting the same activity, albeit at a differentrate or pace. For example, if a user is running at 10 mph and slows to 5mph, he/she may still running, although at a slower pace. In thissituation, however, the frequency detected will be altered.

In one embodiment, step quantification may be determined absent linearcombinations, such as for example, by identifying peaks as discussedabove. The estimate (which may have been adjusted via block 1008) may beused to establish a search range for bounce peak and/or arm swing peakwithin the data (See, e.g., block 1012).

Block 1014 may be implemented to identify a sub-group (or sub-groups) ofpeaks within the frequency data to utilize in the determinations of stepquantification. In one embodiment, a FFT is performed and peaks in theFFT spectrum may be identified, such as with the thresholds and/orderivative around the peak. The performance of the FFT may occur before,during, or after initiating frequency estimation processes, such as oneor more processes described in relation to FIG. 10. In furtherembodiments, the FFT may utilize one or more threshold and derivativesderived from one or more process of flowchart 1000. In on embodiment, aspecific peak (or peaks) within the data (such as for example, dataobtained within the first buffer and/or data obtained during first timeframe) may be utilized. This may be conducted based upon determiningthat linear combination cannot be used. In one embodiment, “bouncepeaks,” “arm swing peaks,” and/or other peaks may be identified. Forexample, many users “bounce” upon landing their feet when running. Thisbounce may provide frequency peaks within the data. Other peaks (and/orvalleys) may be present within the sensor data. For example, many usersoften swing their arms in a predictable manner during running and/orwalking to provide “arm swing peaks”. For example, arms usually swingalong an anterior/posterior axis (e.g., front to back). This frequencymay be about half the frequency of the “bounce peaks”. These peaks,however, may each vary independently, based upon, for example, theindividual, the type of motion, the terrain, and/or a combinationthereof.

FIG. 12A shows graph 1200 of an example FFT output of sensor data, suchas multi-axis accelerometer data. Graph 1200 shows the frequency inHertz (Hz) along x-axis 1202 and FFT power along y-axis 1204. Line 1206plots the frequency (along x-axis 1202) against the power (along y-axis1208), wherein the magnitude or maximum height along the y-axis 1204provides the maximum FFT power for a peak. The peak magnitude indicatesthe relative strength of the frequency, and may be used as an indicatorwhether a person is stepping. Those skilled in the art will appreciatethat graph 1200 is not required to be generated or displayed, but ratheris illustrated herein to demonstrate aspects of this disclosure.

As further seen in FIG. 12A, arm swing range 1208 is shown between about0 and 2 Hz along x-axis 1202 and comprises arm swing peak 1210. Bouncepeak range is shown at about 2-4 Hz along the x-axis 1202 and comprisesbounce peak 1214. Thus, in the illustrated example, the frequency of thebounce peak 1208 within bounce peak range is generally twice thefrequency of the arm swing peak. Thus, systems and methods may identifypeaks (and/or valleys) based upon the established thresholds. In thisregard, computer-executable instructions of one or more non-transitorycomputer-readable mediums may be executed to determine if a thresholdquantity of peaks are located within the range (either fixed ordynamically determined). If no peaks within the range are located, thatbuffer may be emptied (or otherwise not utilize that data in stepcounting determinations). In this regard, the peaks may refer to thefrequencies may be measured by those with the highest quantity ofoccurrences and/or highest absolute value.

Certain embodiments may determine whether peaks (e.g., arm swing peak,bounce peak, and/or any other peak) meet a threshold. In one embodiment,a threshold of the frequency power within the constrained search rangemay ensure that the frequency is not simply noise and that it is largeenough to be considered an activity (such as, for example, walking orrunning). In yet another embodiment, an overlapping window strategy maybe utilized. For example, FFT windows may be analyzed in an overlappingfashion to make sure short term duration steps are counted. FIG. 12Bshows graph 1200 as substantially shown in FIG. 12A, however, furtherincludes arm swing threshold 1216 and bounce threshold 1218. As shown,peaks within arm swing range 1208 (between 0-2 Hz) may only be countedif their magnitude meets a threshold of FFT power (e.g., threshold 1216is at about 500 as shown on the y-axis 1204).

Likewise, in certain embodiments, peaks within bounce peak range (2-4Hz) may only be counted if their magnitude meets a threshold (such asbounce threshold 1218, which is at about 2500 as shown on the on they-axis 1204). In certain embodiments, peaks that meet or exceed athreshold may be counted as steps (see, block 1016). The steps may beincremented for a set time, such as the duration of the FFT analysiswindow. Certain embodiments may continue incrementing with overlappingwindows. In one embodiment, steps may be quantified for each samplebuffer or a certain portion (e.g., 25%) of the analysis buffer and ifthe threshold is met, then steps may be counted for the specific samplebuffer or portion of activity buffer. If, however, the threshold forthat sample buffer or portion is not met, then steps for the remainingportion of the activity buffer (or specific surrounding samples) isdetermined based upon the step frequency. For example, if analysisbuffer comprises 4 sample buffers and only the first 3 have steps, thenthe step count for ¾ of that analysis buffer may be based upon thepreviously selected step frequency.

Further aspects relate to selecting which peaks, if any, are utilized.In accordance with one embodiment, systems and methods may select whichpeaks are to be utilized in quantifying steps despite the fact that thelocated peaks are deemed valid or meet a threshold. As discussed above,bounce data from foot contact may be more reliable arm swing data insome circumstances. Equally, arm swing data may provide more accurateresults in other embodiments. In still further instances, using bothpeaks (and/or others) together to derive a range of data may provide thebest results. Embodiments disclosed herein relate to systems and methodsthat may be used on a portable device configured to be worn on anappendage (such as an arm or leg) to collect activity data and determinewhich peaks to utilize in quantifying steps (and possibly in furtherembodiments, activity type and/or energy expenditure). In this regard,combinations of various peaks may be used to determine specificactivities of the athlete. In certain embodiments, systems and methodsmay be configured to dynamically determine whether to use bounce peaks,such as for example peak 1214 or arm swing peaks, such as peak 1210. Thedetermination may be updated in substantially real-time (such as every0.5 seconds, 1 second, 2 seconds, 4 seconds, etc.) and based upon theactivity data.

FIG. 13 shows example flowcharts that may be implemented to determinewhether to utilize arm swing frequency or bounce frequency in accordancewith one embodiment. As shown in FIG. 13, the systems and methods may beimplemented to select relevant frequency peaks out of the illustrativeFFT output to determine which data provides the most accurate results(e.g., a which frequency from an FFT analysis of accelerometer datashould be utilized). In certain embodiments, step frequency may be usedin the generation of a step count for the period of time represented bythe FFT spectrum.

In one embodiment, the “relevant” peaks may include arm swing peaks andbounce peaks. Block 1301 may be implemented to quantify the number ofidentified peaks within the corresponding search range. Thus, the bouncepeaks located in the frequency estimation for the bounce range (“BR”)(see, e.g., range 1208 comprising frequencies between 0-2 Hz of FIG.12A) may be quantified and the arm swing peaks located in the frequencyestimation for arm swing range (“ASR”) (e.g., range 1212 comprisingfrequencies between 2-4 Hz of FIG. 12A) may also be quantified. Incertain embodiments, the quantity of identified peaks (and/or quantityof specific peaks identified) may be utilized to determine which of theestimated step frequencies (e.g., determined by the ASR, BR or peaks inother ranges) may utilized. For example, decision 1302 may determinewhether there is at least 1 peak in the BR or at least 1 peak in theASR. If not, block 1304 may be implemented to register that no stepswere performed in the specified range. If, however, there is at least 1BR or at least 1 ASR peak at decision 1302, decision 1306 may beimplemented to determine if there is only 1 BR peak (and zero ASR peaks)or alternatively, if there is 1 ASR peak (and zero BR peaks). If it isdetermined that there is only the 1 ASR peak, then block 1308 may beimplemented to mark the step frequency at 2*ASR frequency.Alternatively, if it is determined that there is the one BR peak, thenblock 1310 may be implemented to mark the step frequency ascorresponding to the BR frequency. As a third alternative, if there aremore than only 1 ASR or only 1 BR in absence of each other, thendecision 1312 may be implemented. Before discussing decision 1312, it isworth noting to the reader that FIG. 13 (and other flowcharts providedherein) include several decisions, such as for example, decisions 1302,1306, 1312 and 1314. Those skilled in the art with the benefit of thisdisclosure will readily appreciate that one or more decisions may begrouped into a single decision and/or placed in different order, such asincorporating decision 1304 within decision 1302. Thus, the use of aplurality of decisions in the current order is merely for illustrativepurposes.

One or more processes may determine whether there are exactly 1 BR peakand 1 ASR peak (see, e.g., decision 1312). If not, block 1324 (which isdiscussed below) may be implemented. If so, however, decision 1314 maybe implemented to determine whether the ASR peak is within a set rangeof the BR peak. In one embodiment, decision 1314 may determine whetherthe ASR peak is within +/−15% of the ½*BR peak. If so, block 1316 may beimplemented to determine that the step frequency is the mean of the BRpeak and 2× the ASR frequency.

If, however, the ASR peak and the BR peak are not within the identifiedrange threshold, then block 1318 may be implemented to calculate thedistance from the estimated frequency for each peak. One or moreprocesses may then determine whether the magnitude of at least one ofthe peaks is greater than a threshold. For example, decision 1320 may beimplemented to determine if the magnitude of both peaks are greater thana threshold. If the threshold(s) of decision 1320 are not satisfied,block 1321 may be implemented to select the frequency and magnitude ofthe larger of the two peaks. If the magnitude of the peaks, however, aregreater than a threshold, then step frequency and peak magnitude may bechosen from the peak closer to the estimated step frequency (e.g., block1322).

Looking to FIG. 13B showing flowchart 1323, systems and methods may beconfigured to determine the step frequency when there are more than 1 BRpeak and more than 1 ASR peak in the search range. In one embodiment,block 1324 may be utilized determine the step frequency when there aremore than 1 BR peak and 1 ASR peak in the data. Block 1324 may beimplemented upon determining that there is not exactly 1 BR peak and 1ASR peak at decision 1312 of FIG. 13A, yet in other embodiments, block1324 is independent of decision 1312 and/or FIG. 13A. Block 1324 maydetermine peak proximity to an estimated frequency, such as thefrequency estimated by a frequency estimator (see, e.g., block 1002 andflowchart 1000). In one embodiment, the BR peak and ASR peak closest theestimated frequency are determined. Decision 1326 may be implemented todetermine whether at least one identified ASR peak is within a set rangeof the BR peak and/or whether at least one identified BR peak within aset range of the ASR peak. In one embodiment, decision 1326 maydetermine whether the ASR peak is within +/−15% of the ½*BR peak orwhether the BR peaks are within +/−15% of ½*ASR peak.

If it's determined at decision 1326 that the threshold range set is notmet, then block 1328 may be initiated to default to a search range witha single peak and locate the largest peak in the multi-peak region.Alternatively, block 1330 may be implemented if a criterion set forth indecision 1326 is satisfied. In one embodiment, if there are multiplepeaks within the set range set forth in decision 1326 (e.g., 15%) of thesingle peak range, block 1330 may be implemented to select the frequencyand peak magnitude for the biggest peak. Decision 1332 may beimplemented to determine which of the identified peaks are larger. Forexample, decision 1332 may determine whether the BR peak is larger thanthe ASR peak (or vice versa). Decision 1332 may merely determine whichof the BR peak and the ASR peak is larger. In one embodiment, the largerof the two peaks may be selected as the step frequency (see, e.g.,blocks 1334 and 1336).

FIG. 14 depicts a flowchart 1400 which may represent one or moreprocesses to compare one or more calculated attributes from receivedsensor data with one or more expert models. Accordingly, FIG. 14 may bedescribed as an expert selection module. Those of ordinary skill in theart will readily understand statistical analysis methodology describedas a mixture of experts. Accordingly, in one implementation, an expertmay be a model that may be used to estimate an output value. In oneexample, an output value may be an estimation of oxygen consumptionassociated with one or more activities being performed by a user.

As described briefly in relation to FIG. 6, in one example, 16 experts(models) may be stored in a device, such as device 112, 126, 128, 130,and/or 400. Additionally, there may be a number of attributes (otherwisereferred to as weights) stored in association with each expert. In oneexample, 20 attributes may be compared with 20 weights associated witheach expert model, and wherein said attributes are calculated accordingto those processes described in relation to FIGS. 9A-9E and FIGS.17A-17E, and the like.

In response to receipt, at block 1402, of calculated attributes, weightsassociated with each stored expert model may be selected (block 1404).For each expert model, a probability may be calculated that said expertmodel is the best match for those received attributes, wherein the bestmatch represents a model that will predict with highest accuracy anoutput of the model, given the calculated attribute inputs. In oneexample, block 1406 may represent one or more processes to calculate aprobability that an expert is a best match for received attributes usinga Softmax regression function, and according to a “mixture of experts”methodology, and the like.

Accordingly, given, in one example, 20 input attributes calculated fromsensor data received from an accelerometer, and indicative of anactivity being performed by a user, those 20 attributes may be comparedto an equal number of weights associated with each of the models storedin the energy expenditure system. A calculated Softmax regression mayreturn a probability value associated with each stored model, whereineach probability value represents a likelihood that the associated modelwill give a best available estimate of, in one example, a volume ofoxygen being consumed by a user. For example, an energy estimationsystem may store four models (experts). Accordingly, using attributescalculated from received acceleration sensor data, a Softmax regressionmay be calculated for each of the four models. In one example, thisSoftmax regression may calculate a probability for each of the fourmodels. Accordingly, the model corresponding to the highest calculatedprobability is the model to be used to give a best available estimate ofa volume of oxygen being consumed by a user. In one implementation, aSoftmax regression may be calculated using the formula:

$\begin{matrix}{\left( {{Softmax}\mspace{14mu}{regression}\mspace{14mu}{equation}} \right){p_{i} = \frac{e^{m\frac{T}{t}x}}{\sum\limits_{k}e^{m\frac{T}{k}x}}}} & \;\end{matrix}$

As described in the Softmax regression equation above, p_(i) representsthe probability that model i is the best model, from k different models,to use to predict a volume of oxygen consumption, given the vector ofinput attributes, symbolized by vector x, calculated from sensor inputvalues. Furthermore, m_(i) is a vector of weights associated with modeli.

Block 1406 may produce one or more outputs representing one or moreprobabilities that an associated expert model represents a best-much forcalculated attribute values. Block 1408 may select an expert with ahighest probability from those outputs of block 1406. Accordingly, block1408 may estimate an output using the selected expert, and wherein theoutput may be an estimated volume of oxygen consumption by a user, andbased on one or more activities being performed by the user, and thelike.

Further aspects relate to systems and methods for calculating one ormore other metrics, such as the athlete's speed, distance, and or otherone or more parameters. In one embodiment in which speed is an attributebeing calculated, a speed calculation module may be provided. Providedwith the relevant attributes for the window of interest, systems andmethods may be implemented to determine whether to calculate speed forone or more windows of data. In one implementation, if a data window isdetermined to contain walking or running data, the relevant attributesmay be utilized, such as by being sent to a speed and distance module,to calculate speed and distance. Thus, it goes from the foregoing thatcertain aspects of this disclosure relate to determinations of speed ordistance that comprises categorizing athletic data. As discussed above,certain aspects relate to calculating energy expenditure values withoutcategorizing the athletic data into activity types (walking, running,basketball, sprinting, soccer, football, etc.), however, categorizing atleast a portion of the same data utilized to calculate energyexpenditure for the calculation of other metrics, such as for example,speed and/or distance is within the scope of this disclosure. Speed (oranother parameter determined from the categorization of athletic datamay) be computed from the same attributes utilized for the determinationof energy expenditure values. In this regard, the same exact set ofattributes may be used, however, in yet another embodiment, a sub-set ofthe same attributes may be used; and in yet additional embodiments,there may be only a single common attribute value utilized in thedetermination of two different metrics (such as, for example, speedand/or energy expenditure). In one implementation, speed (or anothermetric) may be determined from at least a portion of data derived fromthe determination of energy expenditure values.

FIGS. 15 and 16 are flowcharts showing example processes that may beutilized in categorizing the athlete's activity (such as, for example,into categories of: “walking”, “running”, or “other”) based upon atleast a portion of the attributes utilized in the calculation of energyexpenditure values. Although example embodiments described hereinutilize the categories “walking”, “running” and/or “other”, thoseskilled in the art will appreciate that this is for illustrativepurposes and other categories are within the scope of this disclosure.Further, although the categories of FIGS. 15 and 16 are provided in theexemplary context of determining speed and/or distance, this is not arequirement. In this regard, categorization of activity data may beperformed independently of any determinations of speed and/or distance.

Looking first to FIG. 15, one or more processes may be utilized to firstdetermine whether the data is to be considered within a first sub-group(e.g., inclusive of walking and running) or, alternatively, within asecond sub-group (e.g., other than walking or running). In one example,one or more processes may be utilized to determine whether data is to beconsidered within a linear travel motion sub-group, or alternatively, alinear motion sub-group. This linear travel motion sub-group may, in oneexample, correspond to walking and/or running. Furthermore, those ofordinary skill will understand that the linear travel motion sub-groupincludes substantially linear motion, such as that associated withwalking or running, and as opposed to that comparatively random movementof a person participating in a team sport, such as basketball, soccer,ice hockey, and the like. Accordingly, running and walking may bereferred to/categorized as “linear travel motion” in comparison to thestop-start/comparatively more intermittent nature of user motion whileparticipating in other athletic activities, such as team sports. Inanother example, however, the linear travel motion of running or walkingmay include running and/or walking around a track, wherein certainperiods of running and/or walking may be considered circular motion, andthe like.

Values for attributes (such as one or more of the attributes disclosedherein and/or known in the art) may be received (e.g., see block 1502).In one embodiment, a 128 sample window of acceleration data at 25 Hz maybe utilized. In one embodiment, a wrist-mounted accelerometer, which maybe a multi-axis accelerometer, may be utilized to capture data.

Step rate may be a criterion utilized to categorize the activity data.Step rate may be an attribute utilized in the determination of energyexpenditure, derived from an attribute, and/or independently derived forcategorization purposes. In certain embodiments, the step rate may becompared to one or more thresholds (see, e.g., block 1504 of FIG. 15A).As used herein, recitation of a second, third or any subsequentthreshold (or other values) is merely for descriptive purposes todistinguish from earlier-mentioned thresholds and not limited tospecific ordering and/or quantity of threshold or ranges. Specificattributes and ordering of the attributes may be based upon whether thestep rate meets designated thresholds. For example, if the step ratedoes not exceed a first threshold (see, e.g., block 1504), it may thenbe determined whether the integral of the accelerometer data from theaxis with highest acceleration values is greater than a 1st threshold(see, e.g., block 1506), wherein if the if the step rate exceeds thefirst threshold for step rate, it may then be determined whether theintegral of the accelerometer data from the axis with highestacceleration values is greater than a 2^(nd) threshold (as opposed tothe first threshold) set for that same attribute (see, e.g., decisionblock 524 of FIG. 15B).

The remainder of FIG. 15A will be described in the context of thedetermination that the step rate met the first threshold, and FIG. 15Bwill be described in the context of the step rate not meeting the firstthreshold, however, those skilled in the art will readily understandthat such an implementation is merely one example. Utilizing data from asingle axis, such as the axis with the greatest magnitude (e.g.,decisions 1504 and 1524), may be beneficial in certain circumstances torestrain the amount of data needed to be analyzed for reliabledeterminations.

If the integral of the accelerometer data from the axis with highestacceleration magnitude is greater than a 1st threshold set for thatattribute, variation of the data may be examined to determinecategorization of activity. As one example, the mean of the standarddeviation (“std. dev.”) of the data may be considered (see, e.g., block1508). In one embodiment, if the mean of the std. dev. exceeds athreshold, then the data may be too inconsistent to be categorized as“walking” or “running”. This is not to suggest that the user's activitydid not include walking or running, for example, the user may be playingdefensive maneuvers during a basketball game, however, categorizing itas “walking” or running” may not be the most accurate category. As oneexample, merely categorizing a defensive basketball's movements as“walking” and attempting to determine distance from relatively smallersidesteps may not be preferable in certain implementations. In oneembodiment, if the mean of the std. dev. exceeds a threshold, the datamay be categorized as a non-walking and/or non-running category. Forexample, it may be categorized as “other.” (see block 1510).Alternatively, if the mean of the std. dev. does not exceed thethreshold, it may be classified as containing “running” and/or “walking”(see, e.g., block 1512). As explained in more detail below withreference to FIG. 16, further analysis may be performed to designate thedata as either “running” or “walking.”

Returning to exemplary block 1506, the integral of the accelerometerdata from the axis with highest acceleration values may not be greaterthan the 1st threshold set for that attribute, therefore, it may bedetermined whether the integral exceeds a second threshold (see, block1514). In certain embodiments, the second threshold may bequantitatively lower than the first threshold (e.g., a smallervalue—which may be in one example, indicative of a lower amount ofacceleration by a user). If the integral does not meet the secondthreshold, the data may be categorized as not being running or walkingdata (see, block 1510). If, however, the second integral threshold ismet, step frequency power (“step freq. power”) may be utilized tocategorize activity data. For example, if the step freq. power exceeds afirst threshold, it may be categorized as “other” (see, e.g., decision1516 going to block 1510). If it does not exceed the first threshold, itmay be compared to a second threshold (e.g., decision 1518). A negativedetermination at decision 1518 may result in categorizing the data ascategorized as not being walking or running data, whereas an affirmativefinding may result in further analysis (see, e.g., decision 1520). Inone embodiment, the range of sensor data (such as accelerometer dataassociated a plurality of axes—such as x, y and z axes) may be comparedwith a threshold (e.g., block 1520). In one embodiment, if the rangethreshold is not met, the data may be classified as running or walking,whereas if the threshold is met, the data may not be classified aswalking or running.

FIG. 15B will be described in the context of the step rate not meetingthe first threshold (see, e.g., decision 1504 of FIG. 15A), however,those skilled in the art will appreciate that one or more aspects ofFIG. 15A, including the utilization of the first threshold of block1504, may be omitted from one or more processes. In one embodiment, theentirety of flowchart 1500 of FIG. 15 may be omitted. In one suchembodiment, FIG. 15B may show the initial stages of a categorizationfunction.

As discussed above, certain embodiments may utilize the data from anaxis with the highest magnitude. As one example, it may be determinedwhether the integral of the accelerometer data from the axis withhighest acceleration magnitude values is greater than a threshold setfor that attribute (see, e.g., decision 1524). In one embodiment, the“3^(rd) threshold” of decision 1524 may be indicative of less movementalong the respective axis than set by the first and/or secondthresholds. If the threshold is not exceeded, step freq. power (e.g., athird threshold of step freq. power at decision 1526) may be utilized.In one embodiment in which at least three thresholds for step freq.power are utilized in an overall process for categorizing activities,the third threshold (e.g., the threshold utilized at decision 1526) maybe a value that is numerically “in-between” the first and secondthreshold values. In one embodiment, the first threshold value may begreater than the third threshold value; however, the second thresholdvalue may be less than the third value. Yet, in other embodiments, thethreshold value may be greater or less than either one or more otherthresholds for this attribute.

In one embodiment, the range of sensor data (such as accelerometer data)associated a plurality of axes (e.g., x, y and z axes) may be comparedwith one more thresholds (e.g., decisions 1528 and 1530) based uponwhether a threshold of decision 1526 is not met. In one embodiment, if asecond threshold (which may be different than the first threshold ofdecision 1520) may be about half of a third threshold for thisattribute. In one embodiment, one of the thresholds may be within +/−10%of another threshold. In one embodiment, the threshold of decision 1530is the highest threshold for that attribute. If the thresholds ofdecisions 1528 or 1530 are met, the data may not be categorized asrunning or walking. (See, decisions 1528 and 1530 going to block 1510labeled “other”). A determination that the third threshold is not met(e.g., at decision 1530), may result in classifying the data as runningor walking, whereas for the second threshold (see decision 1528),further analysis may be conducted if the threshold is not met. In oneembodiment, if the threshold of decision 1528 is not met, step freq.power (e.g., the fourth threshold of step freq. power at decision 1532)may be utilized. The threshold of block 1532 may be 10%, 5% or less ofany other step freq. power threshold utilized. If the threshold of block1532 is met, the data may be categorized as walking or running, yet ifthe threshold is not met, the data may be classified as not walking orrunning.

FIG. 15C may be viewed in the context of FIGS. 15A and 15B, in which afirst step count threshold was not met (see, e.g., decision 1504 of FIG.15A) and/or an integral of sensor data from the axis with the highestmagnitude was met (e.g., decision 1524 of FIG. 15B), however, thoseskilled in the art will appreciate that one or more aspects of FIG. 15Aand/or FIG. 15B, including the utilization of the specific thresholdsmay be omitted from one or more processes conducted in relation to FIG.15C. In one embodiment, the entirety of flowcharts 1500 of FIG. 15and/or 1522 of FIG. 15B may be omitted. In one such embodiment, FIG.15C, inclusive of any portion of flowchart 1534, may show the initialstages of a categorization function.

In one implementation, a determination may be made whether a step freq.power threshold is met (e.g., at decision 1536). In one embodiment, thethreshold is different than the previous four step freq. powerthresholds discussed in relation to FIGS. 15A and 15B. A negativefinding may result in a subsequent determination whether the absolutevalue (“abs val”) of the median of the data is greater than a firstthreshold (e.g., decision 1538), whereas an affirmative determinationmay result in a subsequent determination weather the abs val of themedian of the data meets a second threshold (e.g., decision 1540).

Looking first to decision 1538, upon determining that the abs valuethreshold is met, the data may be classified as running or walking,whereas if the threshold is not met, the data may not be classified aswalking or running (such as for example, placed within an “other”categorization). At block 1540, however, if the relevant abs valuethreshold is met, the data may be categorized data as not being runningor walking, whereas a negative finding may result in further analysis.

In one embodiment, if a threshold of decision 1540 is not met, the rangeof sensor data (such as accelerometer data) associated a plurality ofaxes (e.g., x, y and z axes) may be compared with one more thresholds(see, e.g., decision 1544). In one embodiment, it may be a fourth rangethreshold that is different than the other thresholds (such as optionalthresholds of decisions 1528 and 1530). In one embodiment, if thethreshold of block 1544 is not met, the data may be categorized asrunning or walking. (See, decision 1544 going to block 1512). If thethreshold of decision 1544 is met, then an integral threshold, such asfrom the axis with the highest magnitude may be utilized (see, e.g.,decision 1548), whereas if the threshold is met, then the data isclassified as walking or running data, and wherein if it's not met, thenfurther analysis may be conducted.

In accordance with one implementation, it may be determined whether theabsolute value (“abs val”) of the median of the data is greater than athreshold (e.g., decision 1550). The threshold may be less than one ormore other abs val thresholds, such as thresholds utilized in decisions1538 and/or 1540). An affirmative determination at decision 1550 mayresult in the data being categorized as running or walking. A negativefinding may result in considering the range of data across multipleaxes. In one embodiment, it may be determined whether the range ofsensor data (such as accelerometer data) associated a plurality of axes(e.g., x, y and z axes) meets a threshold (e.g., decision 1552). In oneembodiment previously employing a first range threshold (such as, forexample, decision 1544), decision 1552 may employ a second rangethreshold that is greater than the first range threshold. In accordancewith one implementation, if the threshold of decision 1552 is met, thedata may be classified as running or walking, whereas if the thresholdis not met, the data may not be classified as walking or running.

FIG. 16 is a flowchart showing an example implementation of classifyingsensor data as either walking or running (as opposed to, for example, asingle category that contains both). Looking first to flowchart 1600 ofFIG. 16, one or more processes may be utilized to determine whether thedata should be categorized as either “walking” or “running”. In otherembodiments, additional options may be available. Data, such asattribute values, may be received at block 1602. Attribute valuesreceived at block 1502 may be the same attribute values received atblock 1502 and/or utilized to calculate other metrics, such as energyexpenditure, heart rate, and/or others. In this regard, the attributesutilized in block 1602 may include one or more of the attributesdisclosed herein and/or known in the art. In accordance with oneembodiment, attribute values of data previously pre-screened orotherwise deemed to contain walking or running data may be utilized. Forexample, one or more processes set forth in FIGS. 15A-15C may be used tocategorize at least a portion of the received data as containing walkingor running data as opposed to not containing running or walking data.

In accordance with certain embodiments, the data may be categorized aseither walking or running based entirely on one or two of attributevalues. In one embodiment, an attribute may be the trimmed mean of thevector norm (or derivative thereof). In another embodiment, an attributemay relate to an average value of data collected from a plurality ofaxes (such as the x, y, and/or z axes). In one such embodiment, theattribute may be a median acceleration value associated with a pluralityof the axes.

In the example illustrated in FIG. 16, the derivative of the trimmedmean of the vector norm (“der trimmed mean”) may be utilized to eitherclassify the data as either walking or running (see, e.g., decisions1604, 1606, 1616, 1624). In one embodiment, the der trimmed mean may beused as the initial determination distinguishing between walking andrunning. In certain embodiments, the der trimmed mean alone maydetermine the classification within either walking or running. Forexample, if the der trimmed mean falls within a lowest range, then thedata may be classified as walking (see, e.g., decision 1606 stemmingfrom not meeting the threshold of decision 1604).

If the der trimmed mean falls within a second range above the lowestrange, then further analysis may be performed. In one embodiment, thetrimmed mean (as opposed to the derivative of the trimmed mean) may beutilized, whereas data falling below a threshold may be classified asrunning and data meeting the threshold may be classified as walking(see, e.g., decision, 1612). Further embodiments may employ furtherranges of the der. trimmed mean. For example, block 1604 may be utilizedto pre-screen data that is above a threshold, such that to create arange that is above the first and/or second ranges. For example, thecombination of decisions 1604 and 1616 may be utilized to essentiallycreate two ranges (which may be, for example, a third and fourth range).If the data fails to meet the der trimmed range threshold of decision1616, further analysis of one or more attributes may be conducted tocategorize it as either walking or running. In one embodiment, adetermination that the median of the acceleration data associated withall the axes is greater than a first threshold may result incategorizing the data as walking data, yet a negative determination mayresult in categorizing it as walking data (e.g., determination 1618).

Looking back decision 1616, an affirmative finding may result increating additional ranges for the der trimmed mean (see e.g., decision1624; which may be utilized to divide the fourth range into a lower endand an upper end, which may not be equal). Data within the upper end maybe categorized as running data, and data in the lower end may beclassified upon the same attribute(s) as the data within the thirdrange. In one embodiment, data within the lower end of the fourth rangemay be categorized based upon the median sensor data associated with oneor more sensors along multiple axes. In one embodiment, if the medianacceleration value of data associated with all the 3 axes are above athreshold, then the data may be deemed walking, whereas data not meetingthe threshold may be deemed running.

FIGS. 17A-17E comprise elements similar to those depicted in FIGS.9A-9E, and including further details related to one or more attributescalculated from received sensor data. In one example, FIG. 17A depictsan exemplary flowchart 1700 that may include one or more processes forcalculation of one or more attributes from received sensor data. In oneexample, data received from one or more sensors may be validated, andtransformed prior to receipt by block 1702. In another example, however,one or more data points may be communicated directly to block 1702without validation or transformation, and the like.

In one example, FIG. 17A may represent one or more processes tocalculate one or more attributes from one or more received accelerometerdata points. For example, block 1704 of flowchart 1700 may execute oneor more processes to identify an accelerometer axis indicating agreatest amount of acceleration. In one example, and as a result of oneor more sorting processes executed at block 806 of flowchart 800, theaxis corresponding to a greatest amount of acceleration will be anx-axis, and the like. Accordingly, block 1704 may identify those datavalues associated with an x-axis. However, those of ordinary skill inthe art will understand that the acceleration values associated withhighest acceleration may be re-ordered using one or more alternativemethods, and such that an axis representing a highest amend ofacceleration may differ between data points, or may be re-ordered to bea y-axis or a z-axis, and the like.

As previously described, an attribute may generally be a calculatedvalue representing one or more motions of a user, or parts thereof, andwhich may be used to subsequently predict an output from a model.Furthermore, a model may be used to, among others, predict oxygenconsumption by a user during an activity, and the like. A plurality ofdifferent attributes may be calculated from a single data point receivedfrom a sensor, or a group of data points/group of samples, otherwisereferred to as a dataset, and the like. In one example, a group ofattributes used to estimate, among others oxygen consumption by a usermay include, a vector normal oven acceleration data point, a derivativeof vector normal of an acceleration data point, and/or a Fast FourierTransform of vector normal of an acceleration data point, as describedin relation to FIG. 8A. In another example, an attribute may becalculated as an omnidirectional attribute, wherein an omnidirectionalattribute is representative of a motion of a user captured using asensor sensitive in three dimensions, and the like. In one example, anomnidirectional attribute may be calculated using an accelerometersensitive to acceleration along an x-axis, y-axis, and z-axis, and thelike. In yet another example, an attribute may be calculated as aunidirectional attribute. Accordingly, a unidirectional attribute may becalculated from data outputted from a sensor that is sensitive in asingle dimension, and/or to a single variable. In one example, such asensor may be a heart rate sensor that is sensitive to a heart rate of auser, and the like. In one configuration, one or more attributes may begrouped based on said attributes representing variation in sensor data.In yet another configuration, attributes may be calculated andclassified based on a combination of sensor data and biographicinformation of a user. Biographic information may include, among others,a sex, a mass, and/or a height of a user, and the like.

In another example, further specific attributes may be calculated fromdata received from an accelerometer. For example, one attribute may be acalculated absolute value of a median data point associated with theaxis indicating the greatest amount of acceleration from anaccelerometer, and as outlined in relation to exemplary block 1706. Inanother example, an attribute may include a calculation of a medianabsolute deviation of data associated with an axis indicating a greatestamount of acceleration from accelerometer, and as described in relationto block 1708. In a further example, block 1710 is an exemplaryattribute calculation of a range of values associated with the axisindicating the greatest amount of acceleration from accelerometer. Block1712 calculates a trimmed mean attributes from the data received fromthe axis indicating the greatest amount of acceleration fromaccelerometer. Block 1714 is an example of an attribute calculation ofan integral of data values associated with that axis indicating agreatest amount of acceleration from accelerometer. Block 1716 depictsan example process for calculating an interquartile range attribute fromdata received from said axis indicating the greatest amount ofacceleration from accelerometer. Block 1718 calculates a standarddeviation attribute of those data values associated with the axisindicating a greatest amount of acceleration.

Block 1720 represents one or more processes to communicate one or moreattributes to an expert selection module. Accordingly, an expertselection module may comprise one or more processes to compare one ormore calculated attributes to one or more expert models, wherein anexpert model may be used to predict/estimates an output associated withan activity being performed by a user. In one example, such an outputmay be a volume of oxygen consumption by a user, and the like.

FIG. 17B includes a flowchart 1730, wherein flowchart 1730 may includeone or more processes to calculate one or more attributes from receivedsensor data. Similar to FIG. 17A block 1732 of flowchart 1730 representsone or more processes to receive transformed sensor data. Datatransformation is described in further detail in relation to FIG. 8A andFIG. 8B.

Block 1734 of flowchart 1730 may identify accelerometer axis indicatinga second largest amount of acceleration. In one example, and subsequentto one or more transformation processes including one or more axisre-ordering processes, the axis indicating a second largest amount ofacceleration may be a y-axis, and the like.

Block 1736 may represent one or more processes to filter received datausing a moving average filter. Accordingly, a moving average filter maysmooth data by replacing data points with the average of neighboringdata points within a group of data points. In one example, a movingaverage filter may be equivalent to a low pass filter, and the like.Those of ordinary skill in the art will recognize severalimplementations of moving average filters that may be utilized in thisexample implementation. For example, one or more moving average filtersmay be implemented within a numerical computing environment, and thelike.

Furthermore, and as described in relation to block 1738, one or moreprocesses may be executed to smoothen received data. In oneimplementation, one or more processes to smoothen received data may alsoutilize a moving average filter. Accordingly, smoothening may also beaccomplished using one or more functions associated with a numericalcomputing environment.

In one implementation, block 1740 may calculate an attribute of receiveddata as an integral of those data points associated with the axis ofaccelerometer indicating a second largest amount of acceleration, andthe like. Subsequently, the calculated attribute may be communicated toan expert selection module, as described in relation to block 142.

FIG. 17C depicts a flowchart 1750 which may include one or moreprocesses for calculating one or more attributes of received data,wherein the received data may be one or more data points from anaccelerometer, and the like. Accordingly, block 1752 represents one ormore processes to receive transformed data, wherein data transformationsare described in relation to FIG. 8A and FIG. 8B. Alternatively, thoseof ordinary skill will recognize that the received data may not betransformed, such that received data at block 1752 may, alternatively,be raw data received from one or more sensors.

Block 1754 may represent one or more processes to identify dataassociated with an axis of accelerometer indicating a third largestamount of acceleration. In one example, and as described in relation toblock 806 of FIG. 8A, the axis associated with a third largest amount ofacceleration may be a z-axis.

Block 1756 of flowchart 1750 represents one or more processes to filterreceived data using a moving average filter. Those of ordinary skill inthe art will recognize that various moving average filter processes thatmay be used to filter the received data using, among others, a numericalcomputing environment, and the like. Block 1764 may represent one ormore processes to smoothen received data. For example, a numericalcomputing environment may include one or more smoothening functionsand/or processes, wherein said smoothening functions will be readilyunderstood to those of skill in the art. Furthermore, moving averagefilters and smoothening processes are described in relation to blocks1736 and 1738 from FIG. 17. Block 1766 calculates an attributeassociated with an integral of the received data.

Block 1758 of flowchart 1750 may represent one or more processes forcalculating an attribute representing a range of data values associatedwith the axis for presenting a third largest amount of acceleration froman accelerometer. Additionally or alternatively, block 1760 may executeone or more processes to calculate a minimum value attribute associatedwith those data points indicating a third largest amount of accelerationfrom an accelerometer. Block 1762 represents one or more processes tocalculate an interquartile range of data associated with that axisindicating a third largest amount of acceleration associated with anaccelerometer, and the like.

Block 1768 communicates one or more attributes to an expert selectionmodule, wherein said expert selection module is described in relation toFIG. 14.

FIG. 17D is a flowchart diagram 1770 which may represent one or moreprocesses to calculate one or more attributes associated with receivedsensor data. For example, block 1772 may receive transformed sensor datafrom accelerometer. Accordingly, block 1774 may group those data pointsfrom all axes associated with the accelerometer. In one example, anaccelerometer may output data related to one, two, or three orthogonalaxes (x-axis, y-axis, and/or z-axis). Block 1776 represents one or moreprocesses to calculate an interquartile range attribute of the dataassociated with all available axes. Furthermore, block 1778 representsthose processes for calculation of a sum of median absolute deviation ofthe data associated with all available axes. Additionally, block 1780calculates a sum of median data attribute associated with those datapoints from all available axes from the accelerometer.

Subsequent to calculation of one or more attributes, and as representedby block 1782 of flowchart 1770, one or more attributes may becommunicated to an expert selection module. An expert selection modulemay be one or more processes associated with selecting a best-fit modelassociated with calculated attributes, and as described in furtherdetail in relation to FIG. 14.

FIG. 17E is a flowchart diagram representing one or more processes tocalculate attributes associated with received sensor data. In oneexample, flowchart 1786 represents one or more attribute calculationprocesses associated with data received from an accelerometer, and thelike. Accordingly, block 1788 represents one or more processes toreceive transformed accelerometer data. In one implementation, and asdescribed in relation to FIG. 8A, accelerometer data may be transformedby calculation of a vector normal of said data, and the like. In oneexample, block 1790 identifies a calculated vector normal ofaccelerometer data. From said calculated vector normal data, one or moreattributes may be identified.

In one example, block 1751 processes vector normal data using a movingaverage filter. As previously described, those of ordinary skill in theart will recognize various implementations of moving average filterswhich may be utilized in the following disclosure. Subsequently, and asdescribed in relation to block 1753, the filtered data may be processedusing a frequency estimator. Similarly, those of ordinary skill in theart will recognize various frequency estimator functions/processes thatmay be utilized in relation to block 1753. Block 1755 represents one ormore processes to calculate a step rate attribute using a time-domainfrequency, and from received accelerometer data.

Block 1757 represents one or more processes to mean-center receivedvector normal data. Subsequently, and as described in relation to block1759, the mean-centered data may be transformed using a Fast FourierTransform. Those of ordinary skill in the art once again will recognizeone or more processes for carrying out a Fast Fourier Transform. Block1761 represents one or more processes to calculate a step rate usingfrequency-domain information found from the Fast Fourier Transform.Subsequently, block 1763 calculates a peek step frequency powerattribute.

Block 1765 may be executed to downsample the received vector normaldata. Those of ordinary skill in the art will recognize variousdownsampling processes that may be utilized in relation to block 1765.Subsequently, block 1767 may be executed to calculate a derivative ofthe downsampled data. Alternatively, and using the calculated derivativeof the data from block 1767, a minimum and derivative attribute may becalculated at block 1771, an absolute trimmed mean of a derivativeattribute may be calculated at block 1773, and/or a standard deviationof data may be calculated at block 1775, and the like.

Block 1777 represents one or more processes to process sub-windows ofdata. As previously described, a window of data may be a duration oftime measuring, among others, 5.12 seconds in length, and sampling dataat a rate of 25 Hz, thereby producing 128 accelerometer samples, and thelike. A sub-window of data may be, among others, a fraction of a fullwindow. For example, a sub-window may be one quarter of a full window.As such, a sub window may include 128/4=32 data samples, and the like.Block 1779 of flowchart 1786 represents one or more processes tocalculate a mean a value of the data from block 1777, wherein saidcalculation represents an attribute of the received vector normal data,and the like.

Block 1792 calculates one or more zero crossings of the received vectornormal data. Those of ordinary skill in the art will recognize one ormore processes for identifying and/or calculating zero crossings, andthe like. As such, the calculation of one or more zero crossings atblock 1792 represents an attribute of the received data.

Block 1794 represents one or more processes that may be used tocalculate a standard deviation of received vector normal data, whereinsaid standard deviation represents another attribute of the receiveddata. Similarly, block 1795 represents a calculation of an attribute ofthe received data by executing one or more processes to calculate atrimmed mean of the received data, and the like. In a similar manner tothose one or more attributes previously described, a trimmed mean willbe readily understood to those of ordinary skill in the art, andwherein, in one implementation, a trimmed mean is a mean that excludesoutliers from a data set.

Block 1796 may execute one or more processes to calculate a skewattribute of received vector normal data. A skew will be readilyunderstood to those of ordinary skill with knowledge of statistics, andwherein a skew may be a measure of an extent to which a probabilitydistribution is biased, and the like.

Block 1797 may represent one or more processes to calculate aninterquartile range attribute of the received vector normal data.Accordingly, those of ordinary skill in the art will understand one ormore processes for calculation of an interquartile range associated withstatistical analysis of data, and the like.

In one example, and for accelerometer data having data output associatedwith three mutually orthogonal axes (x-axis, y-axis, and z-axis), thoseattributes calculation may include, in addition to those described inrelation to FIGS. 17A-17E: A range of x-axis values (x_range), an armswing frequency (in one implementation, associated with the motion of anappendage of a user) (L_peak_freqA), a trimmed mean of x-axis values(X_trimmed_mean), an interquartile range of z-axis values (Z_iqRange),an arm swing power (in one implementation, associated with motion of anappendage of a user) (L_peak_powA), a difference in a number of vectornormal values above or below a mean, otherwise referred to as a skew(Skew), an integral of x-axis values (X_integral), a step frequencydetermined from a vector normal, and not using Fast Fourier Transform(TimeDomainFrequency), a summation of max of quartered windows (whereina window may be a period of time for which sensor data is outputted froma sensor device) (Sum_max), a standard deviation of a derivative(Der_std), a sum of the standard deviation of x-, y-, and z-axis values(AS_sum_std), an integral of a vector normal (Integral), a stepfrequency power (H_peak_powA), a mean of the standard deviation valuesof quartered windows (Mean_std), a maximum of a vector normal (Max), amedian absolute deviation of the derivative of the vector normal(Der_med_abs_dev), an interquartile range of x-axis values (X_iqRange),a trimmed mean of vector normal (Trimmed_mean), and/or a standarddeviation of x-axis values (X_stddev), among others.

CONCLUSION

Providing an activity environment having one or more of the featuresdescribed herein may provide a user with an experience that willencourage and motivate the user to engage in athletic activities andimprove his or her fitness. Users may further communicate through socialcommunities and challenge one another to participate in pointchallenges.

Aspects of the embodiments have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications andvariations within the scope and spirit of the appended Clauses willoccur to persons of ordinary skill in the art from a review of thisdisclosure. For example, one of ordinary skill in the art willappreciate that the steps illustrated in the illustrative figures may beperformed in other than the recited order, and that one or more stepsillustrated may be optional in accordance with aspects of theembodiments.

We claim:
 1. A unitary apparatus configured to be worn on a user,comprising: a processor; a sensor configured to capture motion data ofthe user; a non-transitory computer-readable medium comprisingcomputer-executable instructions that when executed by the processorperform at least: receiving, from the sensor and via one or morewireless networks, while being worn by the user, a data streamcomprising one or more data points generated as a result of a motion ofthe user; transforming the one or more data points into a datasetrepresenting a motion of the user; without classifying the one or moredata points into an activity type, calculating a first set of one ormore motion attributes from the dataset; determining, for each model ofone or more models, a probability that each model is a best-match to thefirst set of one or more motion attributes based on a regressionfunction and a comparison of the first set of one or more motionattributes from the dataset to a second set of one or more motionattributes in each model of the one or more models, wherein the secondset of one or more motion attributes and the first set of one or moremotion attributes are independent of any activity type, and wherein thefirst set and the second set have a same number of motion attributes;selecting, based on the regression function and a result of thecomparison of the first set of one or more motion attributes from thedataset to the second set of one or more motion attributes of the one ormore models, a first model, from the one or more models, as a best-matchto the first set of one or more motion attributes from the dataset,wherein the first model is selected as the best-match based on adetermination that the first model is associated with a highestprobability of each of the probabilities; and inputting, into the firstmodel, the first set of one or more motion attributes from the dataset;receiving, from the first model, an output indicating one or morecharacteristics associated with the motion of the user.
 2. The unitaryapparatus of claim 1, wherein the one or more models comprise one ormore energy expenditure models and one or more activity classificationmodels.
 3. The unitary apparatus of claim 2, wherein an energyexpenditure model, selected from the one or more energy expendituremodels, is utilized to calculate an energy expenditure characteristicassociated with the motion of the user.
 4. The unitary apparatus ofclaim 2, wherein an activity classification model, selected from the oneor more activity classification models, is utilized to classify themotion of the user as a linear travel motion.
 5. The unitary apparatusof claim 4, wherein the activity classification model further classifiesthe linear travel motion as running or walking.
 6. The unitary apparatusof claim 4, wherein the activity classification model, selected from theone or more activity classification models, calculates a pace of theuser.
 7. The unitary apparatus of claim 1, wherein the non-transitorycomputer-readable medium comprises instructions that when executed bythe processor further perform at least: validating the one or more datapoints by comparison to one or more threshold values.
 8. The unitaryapparatus of claim 1, wherein the data points generated by the user'smotion are generated during a performance of a first activity type, andwherein at least one of the one or more models is devoid of motion datacollected during any individual's performance of the first activitytype.
 9. The unitary apparatus of claim 1, wherein the first set of oneor more motion attributes are calculated as omnidirectional attributesfrom sensor data that includes a value for each of an x-axis, a y-axis,and a z-axis.
 10. The unitary apparatus of claim 1, wherein the firstset of one or more motion attributes are calculated as unidirectionalattributes from sensor data that includes a value for one of an x-axis,a y-axis, or a z-axis.
 11. The unitary apparatus of claim 1, wherein thefirst set of one or more motion attributes are calculated from acombination of one or more data points captured from the sensor andbiographic data related to the user.
 12. The unitary apparatus of claim3, wherein the non-transitory computer-readable medium further comprisesinstructions that when executed by the processor further perform atleast: after selecting the energy expenditure model from the one or moreenergy expenditure models, categorizing the motion data of the user intoan activity type.
 13. The unitary apparatus of claim 1, wherein thesensor is a first sensor comprising an accelerometer, and wherein thenon-transitory computer-readable medium further comprises instructionsthat when executed by the processor further perform at least: receivingheart rate data obtained from the user during the user's performance ofthe motion detected by the sensor; and determining to cease utilizingheart rate data despite being accurate based upon the accelerometerproviding data indicative that the user has reduced their physicalactivity below a threshold amount for at least a first time frame. 14.The unitary apparatus of claim 1, wherein the capturing from the sensoris conducted while being worn on an appendage of the user.
 15. Acomputer-implemented method, comprising: receiving, by one or moreprocessors, from a sensor, a data stream comprising one or more datapoints generated as a result of a motion of a user; transforming, by theone or more processors, the one or more data points into a datasetrepresenting a motion of the user; without classifying the one or moredata points into an activity type, calculating, by the one or moreprocessors, a first set of one or more motion attributes from thedataset; determining, by the one or more processors, for each model ofone or more models, a probability that each model is a best-match to thefirst set of one or more motion attributes based on a regressionfunction and a comparison of the first set of one or more motionattributes from the dataset to a second set of one or more motionattributes in each model of the one or more models, wherein the secondset of one or more motion attributes and the first set of one or moremotion attributes are independent of any activity type, wherein thefirst set and the second set have a same number of motion attributes;selecting, by the one or more processors, based on the regressionfunction and a result of the comparison of the first set of one or moremotion attributes from the dataset to the second set of one or moremotion attributes of the one or more models, a first model, from the oneor more models, as a best-match to the first set of one or more motionattributes from the dataset, wherein the first model is selected as thebest-match based on a determination that the first model is associatedwith a highest probability of each of the probabilities; inputting, bythe one or more processors, into the first model, the first set of oneor more motion attributes from the dataset; and receiving, by the one ormore processors, from the first model, an output indicating one or morecharacteristics associated with the motion of the user.
 16. Thecomputer-implemented method of claim 15, wherein the one or more modelscomprise one or more energy expenditure models and one or more activityclassification models.
 17. The computer-implemented method of claim 16,wherein an activity classification model, selected from the one or moreactivity classification models, is utilized to classify the motion ofthe user as a linear travel motion.
 18. The computer-implemented methodof claim 15, wherein the data points generated by the user's motion aregenerated during a performance of a first activity type, and wherein atleast one of the one or more models is devoid of motion data collectedduring any individual's performance of the first activity type.
 19. Thecomputer-implemented method of claim 15, wherein the first set of one ormore motion attributes are calculated as either: (a) omnidirectionalattributes from sensor data that includes a value for each of an x-axis,a y-axis, and a z-axis, or (b) unidirectional attributes from sensordata that includes a value for one of an x-axis, a y-axis, or a z-axis.20. The computer-implemented method of claim 15, wherein the first setof one or more motion attributes are calculated from one or more datapoints representing variation between the motion data point outputs fromthe sensor.